Systems and methods for structured illumination microscopy

ABSTRACT

The technology disclosed relates to structured illumination microscopy (SIM). In particular, the technology disclosed relates to capturing and processing, in real time, numerous image tiles across a large image plane, dividing them into subtiles, efficiently processing the subtiles, and producing enhanced resolution images from the subtiles. The enhanced resolution images can be combined into enhanced images and can be used in subsequent analysis steps.

PRIORITY APPLICATIONS

This application is a continuation of U.S. Non-Provisional patentapplication Ser. No. 17/075,692, entitled, “Systems and Methods forStructured Illumination Microscopy”, filed Oct. 21, 2020, which claimsthe benefit of U.S. Provisional Patent Application No. 62/924,130,entitled, “SYSTEMS AND METHODS FOR STRUCTURED ILLUMINATION MICROSCOPY,”filed Oct. 21, 2019, and claims the benefit of U.S. Provisional PatentApplication No. 62/924,138 entitled, “INCREASED CALCULATION EFFICIENCYFOR STRUCTURED ILLUMINATION MICROSCOPY,” filed Oct. 21, 2019, thecontents of which are incorporated by reference herein in theirentirety.

INCORPORATIONS

The following are incorporated by reference for all purposes as if fullyset forth herein: U.S. Provisional Application No. 62/692,303, entitled“Device for Luminescent Imaging” filed on Jun. 29, 2018 (unpublished)and US Nonprovisional patent application “Device for LuminescentImaging” filed on Jun. 29, 2019 (Attorney Docket No. IP-1683-US).

FIELD OF THE TECHNOLOGY DISCLOSED

The technology disclosed relates to structured illumination microscopy(SIM). In particular, the technology disclosed relates to capturing andprocessing, in real time, numerous image tiles across a large imageplane, dividing them into subtiles, efficiently processing the subtiles,and producing enhanced resolution images from the subtiles. The enhancedresolution images can be combined into enhanced images and can be usedin subsequent analysis steps.

The technology disclosed relates to structured illumination microscopy.In particular, the technology disclosed relates to reducing thecomputation required to process, in real time, numerous image tilesacross a large image plane and producing enhanced resolution images fromimage tiles/subtiles. During some intermediate transformations in theSIM processing chain, nearly half of the multiplications and divisionsotherwise required can be replace by lookup operations using theparticular exploitations of symmetry that are described.

BACKGROUND

The subject matter discussed in this section should not be assumed to beprior art merely as a result of its mention in this section. Similarly,a problem mentioned in this section or associated with the subjectmatter provided as background should not be assumed to have beenpreviously recognized in the prior art. The subject matter in thissection merely represents different approaches, which in and ofthemselves can also correspond to implementations of the claimedtechnology.

More than a decade ago, pioneers in structured illumination microscopyreceive a Nobel Prize for Physics. Enhancing image resolution, beyondthe Abbe diffraction limit, was an outstanding development.

Both 2D and 3D SIM have been applied to imaging biological samples, suchas parts of individual cells. Much effort has gone into and manyalternative technical variations have resulted from efforts to study theinterior of cells.

Resolving millions of sources spread across an image plane presents amuch different problem than looking inside cells. For instance, one ofthe new approaches that has emerged, combines numerous images withspecular illumination to produce an enhanced resolution image afterextensive computation. Real time processing of a large image plane withmodest resources requires a radically different approach than suchrecent work has taken.

Accordingly, an opportunity arises to introduce new methods and systemsadapted to process large image planes with reduced requirements forcomputing resources.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee. The color drawings also may be available in PAIRvia the Supplemental Content tab. In the drawings, like referencecharacters generally refer to like parts throughout the different views.Also, the drawings are not necessarily to scale, with an emphasisinstead generally being placed upon illustrating the principles of thetechnology disclosed. In the following description, variousimplementations of the technology disclosed are described with referenceto the following drawings, in which:

FIG. 1A shows Moiré fringe formation by using a grating with onedimensional (1D) modulation.

FIG. 1B presents a graphical illustration of illumination intensitiesproduced by a two dimensional (2D) structured illumination pattern.

FIG. 1C illustrates an example geometrical pattern for nanowellarrangement.

FIG. 2 illustrates a structured illumination microscopy imaging systemthat may utilize spatially structured excitation light to image asample.

FIG. 3 is an optical system diagram illustrating an example opticalconfiguration of two-arm SIM image system.

FIGS. 4A and 4B are example schematic diagrams illustrating opticalconfiguration of a dual optical grating slide SIM imaging system.

FIG. 5A illustrates undesired changes in spacing parameter that mayoccur in SIM imaging systems.

FIG. 5B illustrates undesired changes in phase parameter that may occurin SIM imaging systems.

FIG. 5C illustrates undesired changes in angle of orientation parameterthat may occur in SIM imaging systems.

FIG. 6 illustrates simplified illumination fringe patterns that may beprojected onto the plane of a sample by vertical and horizontal gratingsof SIM imaging system.

FIG. 7 illustrates simplified illumination fringe patterns that may beprojected onto the plane by a first and a second gratings of a dualoptical grating slide SIM imaging system.

FIG. 8A is a simplified depiction of bending parallel lines due todistortion of a lens that magnifies.

FIGS. 8B and 8C illustrate spacing between nominally parallel lines.

FIG. 8D shows an example of subtiles or subfields of a full field ofview (FOV) image.

FIG. 9 generally depicts a color-coded surface that nicely fits observeddata points indicated in red.

FIGS. 10A and 10B illustrate inverted bowl shape of measured spacingrelative to spacing in a near-center subfield.

FIGS. 11A, 11B, and 11C compare measured spacing distortion withquadratic and cubic surface fits.

FIG. 12A illustrates measured data points not smoothed by curve fitting.

FIG. 12B illustrates actual data that are compared to a quadraticallyfitted surface.

FIGS. 13A to 13E illustrate improvement in curve fitting by croppingalong the sensor border.

FIG. 14A graphically illustrates reduction in mean squared error (MSE)at different shrink factors.

FIGS. 14B to 14G illustrate improvement in quadratic fit for angledistortion model by incrementally increasing the shrink factor appliedto the full FOV image data.

FIG. 14H is a graphical plot illustrating marginal improvement inquadratic surface fit when the value of shrink factor is six.

FIGS. 15A and 15B illustrate a quadratic fit of a response surfaceobtained by fitting the fringe spacing distortion (A) and the fringeangle distortion (B) across the full FOV image data.

FIG. 16A illustrates translation of subtile phase offset from subtilecoordinate system to full FOV image coordinate system.

FIG. 16B illustrates position of a point in a subtile with respect tosubtile coordinate system and full FOV image coordinate system.

FIG. 17 is an example phase bias lookup table for subtiles in the fullFOV image.

FIG. 18 is a high-level overview of process steps in the non-redundantsubtile SIM image reconstruction algorithm.

FIG. 19 illustrates symmetries in Fourier space for matrices with evenand odd number of rows and columns.

FIG. 20 illustrates non-redundant and redundant halves of the fullmatrices in frequency domain representing three acquired images acquiredwith one illumination peak angle.

FIG. 21 illustrates reshaping of non-redundant halves of the threematrices in frequency domain representing the three images acquired withone illumination peak angle.

FIG. 22 illustrates band separation process by multiplication of theinverse band separation matrix with the reshaped matrix from FIG. 21 .

FIG. 23A illustrates an example of (1, 1) shift operation applied to amatrix.

FIGS. 23B and 23C illustrate averaging of first and middle columns innon-redundant SIM image reconstruction.

FIG. 24 is a simplified block diagram of a computer system that can beused to implement the technology disclosed.

FIGS. 25A and 25B present improvements in error rate and percentage ofclusters passing filter (% PF) metrics with updates in SIM imagereconstruction algorithm.

FIGS. 26A to 26C present graphical illustrations of improvements inerror rate over multiple cycles in sequencing runs.

FIG. 26D presents graphical plots of improvement in error rates byinclusion of phase learning and stitch fixing techniques in the BaselineSIM algorithm.

DETAILED DESCRIPTION

The following discussion is presented to enable any person skilled inthe art to make and use the technology disclosed, and is provided in thecontext of a particular application and its requirements. Variousmodifications to the disclosed implementations will be readily apparentto those skilled in the art, and the general principles defined hereinmay be applied to other implementations and applications withoutdeparting from the spirit and scope of the technology disclosed. Thus,the technology disclosed is not intended to be limited to theimplementations shown, but is to be accorded the widest scope consistentwith the principles and features disclosed herein.

INTRODUCTION

Structured illumination microscopy in two and three dimensions hashelped researchers investigate the internal structure of living cellsand even the molecular arrangement of biological materials, because itimproves the resolution of image capture systems. See, e.g., Sydor,Andrew & Czymmek, Kirk & Puchner, Elias & Mennella, Vito. (2015).Super-Resolution Microscopy: From Single Molecules to SupramolecularAssemblies. Trends in Cell Biology. 25. 10.1016/j.tcb.2015.10.004; also,Lal et al. 2015, A., Shan, C., & Xi, P. (2015). Structured illuminationmicroscopy image reconstruction algorithm. IEEE Journal of SelectedTopics in Quantum Electronics, 22(4), 50-63. For readers who desire arefresher on SIM processing, Lal et al. 2015 is an excellent combinationof illustrations and mathematical exposition, short of deriving theequations used. This disclosure extends SIM technology to imageprocessing of flow cells with several technologies that can be usedindividually or in combination.

SIM holds the potential to resolve densely packed samples, from flowcells with fluorescent signals from millions of sample points, therebyreducing reagents needed for processing and increasing image processingthroughput. The trick is to resolve densely pack fluorescent samples,closer together even than the Abbe diffraction limit for resolvingadjoining light sources, using SIM techniques. The samples can be inregularly spaced nanowells or they can be randomly distributed clusters.Most of the description that follows is directed to patterned nanowells,but the SIM technology also applies to randomly distributed clusters.

Technical problems related to lens distortions and requiredcomputational resources emerge from resolving densely packed sources.This disclosure addresses those technical problems.

Structured illumination can produce images that have several times asmany resolved illumination sources as with normal illumination.Information is not simply created. Instead, multiple images with varyingangles and phase displacements of structured illumination are used totransform closely spaced, otherwise unresolvably high spatial frequencyfeatures, into lower frequency signals that can be sensed by an opticalsystem without violating the Abbe diffraction limit. This limit isphysically imposed on imaging by the nature of light and optics and isexpressed as a function of illumination wavelength and the numericalaperture (NA) of the final objective lens. Applying SIM reconstruction,information from multiple images is transformed from the spatial domaininto the Fourier domain, combined and processed, then reconstructed intoan enhanced image.

In SIM, a grating is used, or an interference pattern is generated,between the illumination source and the sample, to generate anillumination pattern, such as a pattern that varies in intensityaccording to a sine or cosine function. In the SIM context, “grating” issometimes used to refer to the projected structured illuminationpattern, in addition to the surface that produces the structuredillumination pattern. The structured illumination pattern alternativelycan be generated as an interference pattern between parts of a splitcoherent beam.

Projection of structured illumination onto a sample plane, for examplein FIG. 1 , mixes the illumination pattern with fluorescent (orreflective) sources in a sample to induce a new signal, sometimes calleda Moiré fringe or aliasing. The new signal shifts high-spatial frequencyinformation to a lower spatial frequency that can be captured withoutviolating the Abbe diffraction limit. After capturing images of a sampleilluminated with a 1D intensity modulation pattern, as shown in FIG. 1A,or 2D intensity modulation pattern, as in FIG. 1B, a linear system ofequations is solved and used to extract from multiple images of theMoiré fringe or aliasing, parts of the new signal that containsinformation shifted from the higher to the lower spatial frequency. Tosolve the linear equations, three or more images are captured with thestructured illumination pattern shifted or displaced in steps. Often,images of varying phases (3 to 7 phases) per angle (with one to fiveangles) are captured for analysis and then separated by bands forFourier domain shifting and recombination. Increasing the number ofimages can improve the quality of reconstructed images by boosting thesignal-to-noise ratio. However, it can also increase computation time.The Fourier representation of the band separated images is shifted andsummed to produce a reconstructed sum. Eventually, an inverse FastFourier Transform (FFT) reconstructs a new high-resolution image fromthe reconstructed sum.

Applying the multiple image and shifting approach only enhances imageinformation in a specific direction along which a 1D structuredillumination pattern is shifted, so the structured illumination orgrating pattern is rotated, and the shifting procedure repeated.Rotations such as 45, 60, 90 or 120 degrees of a 1D pattern can beapplied to produce six or nine images in sets of three steps.Environmental factors such as damage to molecules on the surface of flowcells due to blue and green colored lasers, tilts of tiles on thesurface of flow cells etc., can cause distortions in phase biasestimations for image subtiles or subwindows of the full field of view(FOV) image. They also can cause differences among tiles across asubstrate. More frequent estimation of SIM image reconstructionparameters can be performed to compensate for these environmentalfactors. For example, the phase biases of subtiles can be re-estimatedfor every tile, every cycle of reconstruction to minimize these errors.Angle and spacing parameters do not change as frequently as phase biasand therefore, increasing their estimation frequency can introduceadditional compute that is not necessary. However, angle and spacingparameters can be computed more frequently, if required.

Application of SIM to Flow Cell Image Analysis

Imaging a flow cell with millions of fluorescent samples is more likescanning space with a telescope than like studying micro-structures of aliving cell. Scanning a flow cell with economical optics is more likecapturing images with an instamatic camera than like using the adaptiveoptics of the Mount Palomar Observatory. Scanning a flow cell or the skyinvolves numerous images that cover tiles of the target, as opposed toimaging a living cell in the sweet spot of a lens. The number of imagesrequired to cover any target depends on the field of view of each imagetile and the extent of overlap. An economical lens suffers distortionsat the edges of the field of view, which complicates generation ofenhanced images.

Calculations used in SIM reconstruction are sensitive to lensdistortions, pattern alignment in a particular image set, and thermaleffects on pattern alignment over imaging cycles that progress for hoursand hours. Increasing the field of view, using most of the lens insteadof a sweet spot in the center, makes the reconstruction of the SIMimages susceptible to distortion induced by the aberrations in the lens.These aberrations, such as coma, distort the structured illuminationpattern and make parallel lines of illumination peaks appear to curve,as generally depicted in FIG. 8A, changing the distance betweenbrightness peaks of the pattern and apparent pattern angles.

Practical instruments require characterization that maps distortions andoffsets across subtiles of a tile. Due to the duration of flow cellimaging over cycles, some instruments will benefit from updatedcharacterization at the beginning of a run or even during a run. Thermaldistortion over hours and hours of flow cell imaging in hundreds ofcycles, can make it useful to recharacterize the angle, spacing andphase (shift) of the structured illumination occasionally or regularly.

The angle (rotation) and spacing (scale) of a projected structuredillumination pattern can be determined by fringe peak estimation whenthe instrument is characterized. The estimated phase or displacement ofthe repeating pattern along a direction of stepping can be expressed asa spatial shift of 0 to 360 degrees, or could be expressed in radians.Distortion across the lens complicates parameter estimation, leading toproposals for computationally expensive reconstructions from numerousimages. See, e.g., Ayuk et al. (2013) Structured illuminationfluorescence microscopy with distorted excitations using a filteredblind-SIM algorithm. Optics letters. 38. 4723-6. 10.1364/OL.38.004723;and Mudry et al. (2012) Structured illumination microscopy using unknownspeckle patterns. Nature Photonics. 6. 312-315. The technology disclosedsimplifies repeated parameter estimation and reduces the computationneeded in repeated estimation cycles.

Real time parameter estimation for SIM reconstruction is computationallychallenging. The computing power needed for SIM reconstruction increaseswith a cubic relationship to the number of pixels in an image field orsubfield. For example, for an image with a width of M pixels and aheight of N pixels, the Fourier Transform can have a computationalcomplexity of k*M*N(log(M*N)). Therefore, the order of magnitude ofresources for SIM image reconstruction can increase between quadraticO(N²) and cubic O(N³) as the number of pixels in the images increase.Thus, a two-fold increase in image dimensions such as from 512×512 to1024×1024 can result in up to eight times increase in computationalcost. It is particularly challenging to reconstruct an enhanced imagefrom six or nine images of 20 megapixels on a CPU, at a rate of onecompleted reconstruction each 0.3 seconds, while scanning proceeds. Realtime processing is desirable to reduce storage requirements and trackquality of flow cell processing over hours and hours as scanning andsequencing proceeds. The technology disclosed reduces some corecomputations by approximately one-half, using symmetry and near symmetryin matrices of Fourier domain coefficients.

Addressing Lens Distortion

The technology disclosed addresses lens distortion that is not toosevere, for an optical system within economical manufacturingtolerances, by subdividing a captured image tile into subtiles andhandling a near-center subtile differently than other subtiles. Theimage captured by the optical sensor can be referred to as a tile. Animaging cycle for the flow cell captures many image tiles with someoverlap. Each image tile is divided into independently evaluatedsubtiles. Subtiles can be reconstructed independently of one another,even in parallel. Reconstructions from enhanced subtiles can be stitchedtogether to create a reconstructed tile with enhanced spatialresolution.

The interpolation technology disclosed for relating reconstructionparameters for the near-center subtile to other subtiles approximates anon-linear function, such as a quadratic curve, with a piece-wiseapproximation. The technology disclosed subdivides an image tile intosubtiles such that the peak lines are approximately evenly spaced withina subtile, thereby achieving better image quality from reconstructedsubtiles across a field of view of a lens.

This subtiling approach to mitigate fringe distortion creates a newproblem: reconstruction parameters must be estimated for each subtile.Parameter estimation is the most expensive setup in SIM reconstructionand subtiling makes increases the parameter estimation run time by atleast one order of magnitude, e.g., an image divided into a 5×5 subtileswill create an algorithm that is 25 times slower. A tile divided into8×11 subtiles requires 88 sets of reconstruction parameters instead ofjust one set.

The technology disclosed learns a function that maps the distortion andthereby reduces recalculation in repeated cycles. The learned functionremains valid across cycles and image sets because it maps opticalcharacteristics of the optical system that change when the opticalsystem is changed, e.g., through realignment. During estimation cycles,between instrument characterizations, expensive parameter estimatecomputation focuses on a single, near-center subtile. The parameters foreach subtile map measurements in cycles from the near-center, referencesubtile and to the other subtiles.

Three parameters are mapped for the subtiles: illumination peak angle,illumination peak spacing and phase displacement. The illumination peakangle is also referred to as grating angle. The illumination peakspacing is also referred to as grating spacing. The technology disclosedmaps the angle and spacing using quadratic surface distortion models.The phase displacement or simply the phase is the shift of thestructured illumination pattern or grating as projected onto the sampleplane.

Reusable spacing and angle distortion models are computed a priori usinga subtiling approach to characterize the full FOV. The window or subtileapproach divides a tile into overlapping windows or subtiles of imagesand performs SIM parameter estimation on each subtile. The subtileparameter estimation results are then fed into least squares regression,which generates quadratic surfaces of distortion. Stored coefficients ofthe equations can then be used to extrapolate the parameters of subtilesor at any location in the full field of view image. In alternativeimplementations, the coefficients can be stored and used, or they can betranslated into a subtile a lookup table that relates the near-centersubtile to the other subtiles. A near-center subtile is used as thereference, because the center of a lens suffers less distortion than therim.

For phase relationship among subtiles, a lookup table works better thana curve fit, as presented below.

Over the course of a run that extends hours and hours, periodicrecharacterization can be applied to guard against thermal instabilityand resulting drift. After the extrapolation factors have beenredetermined, the process resumes extrapolating parameter estimates forthe near-center subtile to the other subtiles by extrapolation andwithout expensive parameter estimates for the other subtiles.

Exploiting Symmetry to Reduce Calculation Cost

When imaging a single cell, SIM reconstruction from numerous images isoften done on specialized hardware such as a GPU, FPGA or CGRA. Usingabundant computing resources, reconstruction algorithms work with fullyredundant, center shifted Fourier Transforms. Using, instead, areasonably priced CPU, we disclose SIM reconstruction usingnon-redundant coefficients of Fourier transformed images. Animplementation is described based on symmetries of data in a cornershifted Fourier transform space. Using certain symmetries increasesprogram complexity, but can reduce the amount of data for compute in acore set of calculations by half and the number of calculations requiredby half, while maintaining nearly the same accuracy. We also reducecomputation required to shift Fourier representations of images beingcombined.

Adaptation to 2D Illumination Patterns

The standard algorithms for 1D modulated illumination requiremodification when used with a 2D modulated illumination pattern. Thisincludes illumination peak spacing and illumination peak angleestimation, which requires a 2D band separation instead of a 1D. It alsoincludes Wicker phase estimation, which must work from two points(instead of one) in order to estimate the phase in 2 dimensions. A 1Dinterference pattern can be generated by one dimensional diffractiongrating as shown in FIG. 1A or as a result of an interference pattern oftwo beams.

FIG. 1B illustrates an intensity distribution that can be produced by atwo-dimensional (2D) diffraction grating or by interference of fourlight beams. Two light beams produce an intensity pattern (horizontalbright and dark lines) along y-axis and are therefore referred to as they-pair of incident beams. Two more light beams produce an intensitypattern (vertical bright and dark lines) along x-axis and are referredto as the x-pair of incident beams. The interference of the y-pair withthe x-pair of light beams produces a 2D illumination pattern. FIG. 1Bshows intensity distribution of such a 2D illumination pattern.

FIG. 1C illustrates an arrangement of nanowells at the surface of a flowcell positioned at corners of a rectangle. When using 1D structuredillumination, the illumination peak angle is selected such that imagesare taken along a line connecting diagonally opposed corners of therectangle. For example, two sets of three images (a total of six images)can be taken at +45 degree and −45-degree angles. As the distance alongthe diagonal is more than the distance between any two sides of therectangle, we are able to achieve a higher resolution image. Nanowellscan be arranged in other geometric arrangements such as a hexagon. Threeor more images can then be taken along each of three diagonals of thehexagon, resulting, for instance, in nine or fifteen images.

The new and specialized technologies disclosed can be used individuallyor in combination to improve scanning performance while detectingflorescence of millions of samples distributed across a flow cell, overmultiple cycles. In sections that follow, we introduce terminology,describe an imaging instrument that can be improved using the technologydisclosed, and disclose new image enhancement technologies that can beused individually or in combination.

Terminology

As used herein to refer to a structured illumination parameter, the term“frequency” is intended to refer to an inverse of spacing betweenfringes or lines of a structured illumination pattern (e.g., fringe orgrid pattern) as frequency and period are inversely related. Forexample, a pattern having a greater spacing between fringes will have alower frequency than a pattern having a lower spacing between fringes.

As used herein to refer to a structured illumination parameter, the term“phase” is intended to refer to a phase of a structured illuminationpattern illuminating a sample. For example, a phase may be changed bytranslating a structured illumination pattern relative to an illuminatedsample.

As used herein to refer to a structured illumination parameter, the term“orientation” is intended to refer to a relative orientation between astructured illumination pattern (e.g., fringe or grid pattern) and asample illuminated by the pattern. For example, an orientation may bechanged by rotating a structured illumination pattern relative to anilluminated sample.

As used herein to refer to a structured illumination parameter, theterms “predict” or “predicting” are intended to mean calculating thevalue(s) of the parameter without directly measuring the parameter orestimating the parameter from a captured image corresponding to theparameter. For example, a phase of a structured illumination pattern maybe predicted at a time t1 by interpolation between phase values directlymeasured or estimated (e.g., from captured phase images) at times t2 andt3 where t2<t1<t3. As another example, a frequency of a structuredillumination pattern may be predicted at a time t1 by extrapolation fromfrequency values directly measured or estimated (e.g., from capturedphase images) at times t2 and t3 where t2<t3<t1.

As used herein to refer to light diffracted by a diffraction grating,the term “order” or “order number” is intended to mean the number ofinteger wavelengths that represents the path length difference of lightfrom adjacent slits or structures of the diffraction grating forconstructive interference. The interaction of an incident light beam ona repeating series of grating structures or other beam splittingstructures can redirect or diffract portions of the light beam intopredictable angular directions from the original beam. The term “zerothorder” or “zeroth order maximum” is intended to refer to the centralbright fringe emitted by a diffraction grating in which there is nodiffraction. The term “first-order” is intended to refer to the twobright fringes diffracted to either side of the zeroth order fringe,where the path length difference is ±1 wavelengths. Higher orders arediffracted into larger angles from the original beam. The properties ofthe grating can be manipulated to control how much of the beam intensityis directed into various orders. For example, a phase grating can befabricated to maximize the transmission of the ±1 orders and minimizethe transmission of the zeroth order beam.

As used herein to refer to a sample, the term “feature” is intended tomean a point or area in a pattern that can be distinguished from otherpoints or areas according to relative location. An individual featurecan include one or more molecules of a particular type. For example, afeature can include a single target nucleic acid molecule having aparticular sequence or a feature can include several nucleic acidmolecules having the same sequence (and/or complementary sequence,thereof).

As used herein, the term “xy plane” is intended to mean a 2-dimensionalarea defined by straight line axes x and y in a Cartesian coordinatesystem. When used in reference to a detector and an object observed bythe detector, the area can be further specified as being orthogonal tothe beam axis, or the direction of observation between the detector andobject being detected.

As used herein, the term “z coordinate” is intended to mean informationthat specifies the location of a point, line or area along an axis thatis orthogonal to an xy plane. In particular implementations, the z axisis orthogonal to an area of an object that is observed by a detector.For example, the direction of focus for an optical system may bespecified along the z axis.

As used herein, the term “optically coupled” is intended to refer to oneelement being adapted to impart light to another element directly orindirectly.

SIM Hardware

This section of builds on the disclosure previously made by Applicant inU.S. Provisional Application No. 62/692,303 filed on Jun. 29, 2018(unpublished). Structured illumination microscopy (SIM) describes atechnique by which spatially structured (i.e., patterned) light may beused to image a sample to increase the lateral resolution of themicroscope by a factor of two or more. FIG. 1A shows Moiré fringe (orMoiré pattern) formation by using a grating with 1D modulation. Thesurface containing the sample is illuminated by a structured pattern oflight intensity, typically sinusoidal, to effect Moiré fringe formation.FIG. 1A shows two sinusoidal patterns when their frequency vectors, infrequency or reciprocal space, are (a) parallel and (b) non-parallel.FIG. 1A is a typical illustration of shifting high-frequency spatialinformation to lower frequency that can be optically detected. The newsignal is referred to as Moiré fringe or aliasing. In some instances,during imaging of the sample, three images of fringe patterns of thesample are acquired at various pattern phases (e.g., 0°, 120°, and240°), so that each location on the sample is exposed to a range ofillumination intensities, with the procedure repeated by rotating thepattern orientation about the optical axis to 2 (e.g., 45°, 135°) or 3(e.g. 0°, 60° and 120°) separate angles. The captured images (e.g., sixor nine images) may be assembled into a single image having an extendedspatial frequency bandwidth, which may be retransformed into real spaceto generate an image having a higher resolution than one captured by aconventional microscope.

In some implementations of SIM systems, a linearly polarized light beamis directed through an optical beam splitter that splits the beam intotwo or more separate orders that may be combined and projected on theimaged sample as an interference fringe pattern with a sinusoidalintensity variation. Diffraction gratings are examples of beam splittersthat can generate beams with a high degree of coherence and stablepropagation angles. When two such beams are combined, the interferencebetween them can create a uniform, regularly-repeating fringe patternwhere the spacing is determined by factors including the angle betweenthe interfering beams.

FIG. 1B presents an example of a 2D structured illumination. A 2Dstructured illumination can be formed by two orthogonal 1D diffractiongratings superimposed upon one another. As in the case of 1D structuredillumination patterns, the 2D illumination patterns can be generatedeither by use of 2D diffraction gratings or by interference between fourlight beams that creates a regularly repeating fringe pattern.

During capture and/or subsequent assembly or reconstruction of imagesinto a single image having an extended spatial frequency bandwidth, thefollowing structured illumination parameters may need to be considered:the orientation or angle of the fringe pattern also referred to asillumination peak angle relative to the illuminated sample, the spacingbetween adjacent fringes referred to as illumination peak spacing (i.e.,frequency of fringe pattern), and phase displacement of the structuredillumination pattern. In an ideal imaging system, not subject to factorssuch as mechanical instability and thermal variations, each of theseparameters would not drift or otherwise change over time, and theprecise SIM frequency, phase, and orientation parameters associated witha given image sample would be known. However, due to factors such asmechanical instability of an excitation beam path and/or thermalexpansion/contraction of an imaged sample, these parameters may drift orotherwise change over time.

As such, a SIM imaging system may need to estimate structuredillumination parameters to account for their variance over time. As manySIM imaging systems do not perform SIM image processing in real-time(e.g., they process captured images offline), such SIM systems may spenda considerable amount of computational time to process a SIM image toestimate structured illumination parameters for that image.

FIGS. 2-4B illustrate three such example SIM imaging systems. It shouldbe noted that while these systems are described primarily in the contextof SIM imaging systems that generate 1D illumination patterns, thetechnology disclosed herein may be implemented with SIM imaging systemsthat generate higher dimensional illumination patterns (e.g.,two-dimensional grid patterns).

FIG. 2 illustrates a structured illumination microscopy (SIM) imagingsystem 100 that may implement structured illumination parameterprediction in accordance with some implementations described herein. Forexample, system 100 may be a structured illumination fluorescencemicroscopy system that utilizes spatially structured excitation light toimage a biological sample.

In the example of FIG. 2 , a light emitter 150 is configured to output alight beam that is collimated by collimation lens 151. The collimatedlight is structured (patterned) by light structuring optical assembly155 and directed by dichroic mirror 160 through objective lens 142 ontoa sample of a sample container 110, which is positioned on a motionstage 170. In the case of a fluorescent sample, the sample fluoresces inresponse to the structured excitation light, and the resultant light iscollected by objective lens 142 and directed to an image sensor ofcamera system 140 to detect fluorescence.

Light structuring optical assembly 155 includes one or more opticaldiffraction gratings or other beam splitting elements (e.g., a beamsplitter cube or plate) to generate a pattern of light (e.g., fringes,typically sinusoidal) that is projected onto samples of a samplecontainer 110. The diffraction gratings may be one-dimensional ortwo-dimensional transmissive or reflective gratings. The diffractiongratings may be sinusoidal amplitude gratings or sinusoidal phasegratings.

In some implementations, the diffraction grating(s)s may not utilize arotation stage to change an orientation of a structured illuminationpattern. In other implementations, the diffraction grating(s) may bemounted on a rotation stage. In some implementations, the diffractiongratings may be fixed during operation of the imaging system (i.e., notrequire rotational or linear motion). For example, in a particularimplementation, further described below, the diffraction gratings mayinclude two fixed one-dimensional transmissive diffraction gratingsoriented perpendicular to each other (e.g., a horizontal diffractiongrating and vertical diffraction grating).

As illustrated in the example of FIG. 2 , light structuring opticalassembly 155 outputs the first orders of the diffracted light beams(e.g., m=±1 orders) while blocking or minimizing all other orders,including the zeroth orders. However, in alternative implementations,additional orders of light may be projected onto the sample.

During each imaging cycle, imaging system 100 utilizes light structuringoptical assembly 155 to acquire a plurality of images at various phases,with the fringe pattern displaced laterally in the modulation direction(e.g., in the x-y plane and perpendicular to the fringes), with thisprocedure repeated one or more times by rotating the pattern orientationabout the optical axis (i.e., with respect to the x-y plane of thesample). The captured images may then be computationally reconstructedto generate a higher resolution image (e.g., an image having about twicethe lateral spatial resolution of individual images).

In system 100, light emitter 150 may be an incoherent light emitter(e.g., emit light beams output by one or more excitation diodes), or acoherent light emitter such as emitter of light output by one or morelasers or laser diodes. As illustrated in the example of system 100,light emitter 150 includes an optical fiber 152 for guiding an opticalbeam to be output. However, other configurations of a light emitter 150may be used. In implementations utilizing structured illumination in amulti-channel imaging system (e.g., a multi-channel fluorescencemicroscope utilizing multiple wavelengths of light), optical fiber 152may optically couple to a plurality of different light sources (notshown), each light source emitting light of a different wavelength.Although system 100 is illustrated as having a single light emitter 150,in some implementations multiple light emitters 150 may be included. Forexample, multiple light emitters may be included in the case of astructured illumination imaging system that utilizes multiple arms,further discussed below.

In some implementations, system 100 may include a tube lens 156 that mayinclude a lens element to articulate along the z-axis to adjust thestructured beam shape and path. For example, a component of the tubelens may be articulated to account for a range of sample thicknesses(e.g., different cover glass thickness) of the sample in container 110.

In the example of system 100, fluid delivery module or device 190 maydirect the flow of reagents (e.g., fluorescently labeled nucleotides,buffers, enzymes, cleavage reagents, etc.) to (and through) samplecontainer 110 and waste valve 120. Sample container 110 can include oneor more substrates upon which the samples are provided. For example, inthe case of a system to analyze a large number of different nucleic acidsequences, sample container 110 can include one or more substrates onwhich nucleic acids to be sequenced are bound, attached or associated.The substrate can include any inert substrate or matrix to which nucleicacids can be attached, such as for example glass surfaces, plasticsurfaces, latex, dextran, polystyrene surfaces, polypropylene surfaces,polyacrylamide gels, gold surfaces, and silicon wafers. In someapplications, the substrate is within a channel or other area at aplurality of locations formed in a matrix or array across the samplecontainer 110. System 100 may also include a temperature stationactuator 130 and heater/cooler 135 that can optionally regulate thetemperature of conditions of the fluids within the sample container 110.

In particular implementations, the sample container 110 may beimplemented as a patterned flow cell including a translucent coverplate, a substrate, and a liquid contained therebetween, and abiological sample may be located at an inside surface of the translucentcover plate or an inside surface of the substrate. The flow cell mayinclude a large number (e.g., thousands, millions, or billions) of wells(also referred to as nanowells) or regions that are patterned into adefined array (e.g., a hexagonal array, rectangular array, etc.) intothe substrate. Each region may form a cluster (e.g., a monoclonalcluster) of a biological sample such as DNA, RNA, or another genomicmaterial which may be sequenced, for example, using sequencing bysynthesis. The flow cell may be further divided into a number of spacedapart lanes (e.g., eight lanes), each lane including a hexagonal arrayof clusters.

Sample container 110 can be mounted on a sample stage 170 to providemovement and alignment of the sample container 110 relative to theobjective lens 142. The sample stage can have one or more actuators toallow it to move in any of three dimensions. For example, in terms ofthe Cartesian coordinate system, actuators can be provided to allow thestage to move in the X, Y and Z directions relative to the objectivelens. This can allow one or more sample locations on sample container110 to be positioned in optical alignment with objective lens 142.Movement of sample stage 170 relative to objective lens 142 can beachieved by moving the sample stage itself, the objective lens, someother component of the imaging system, or any combination of theforegoing. Further implementations may also include moving the entireimaging system over a stationary sample. Alternatively, sample container110 may be fixed during imaging.

In some implementations, a focus (z-axis) component 175 may be includedto control positioning of the optical components relative to the samplecontainer 110 in the focus direction (typically referred to as the zaxis, or z direction). Focus component 175 can include one or moreactuators physically coupled to the optical stage or the sample stage,or both, to move sample container 110 on sample stage 170 relative tothe optical components (e.g., the objective lens 142) to provide properfocusing for the imaging operation. For example, the actuator may bephysically coupled to the respective stage such as, for example, bymechanical, magnetic, fluidic or other attachment or contact directly orindirectly to or with the stage. The one or more actuators can beconfigured to move the stage in the z-direction while maintaining thesample stage in the same plane (e.g., maintaining a level or horizontalattitude, perpendicular to the optical axis). The one or more actuatorscan also be configured to tilt the stage. This can be done, for example,so that sample container 110 can be leveled dynamically to account forany slope in its surfaces.

The structured light emanating from a test sample at a sample locationbeing imaged can be directed through dichroic mirror 160 to one or moredetectors of camera system 140. In some implementations, a filterswitching assembly 165 with one or more emission filters may beincluded, where the one or more emission filters can be used to passthrough particular emission wavelengths and block (or reflect) otheremission wavelengths. For example, the one or more emission filters maybe used to switch between different channels of the imaging system. In aparticular implementation, the emission filters may be implemented asdichroic mirrors that direct emission light of different wavelengths todifferent image sensors of camera system 140.

Camera system 140 can include one or more image sensors to monitor andtrack the imaging (e.g., sequencing) of sample container 110. Camerasystem 140 can be implemented, for example, as a charge-coupled device(CCD) image sensor camera, but other image sensor technologies (e.g.,active pixel sensor) can be used.

Output data (e.g., images) from camera system 140 may be communicated toa real-time SIM imaging component 191 that may be implemented as asoftware application that, as further described below, may reconstructthe images captured during each imaging cycle to create an image havinga higher spatial resolution. The reconstructed images may take intoaccount changes in structure illumination parameters that are predictedover time. In addition, SIM imaging component 191 may be used to trackpredicted SIM parameters and/or make predictions of SIM parameters givenprior estimated and/or predicted SIM parameters.

A controller 195 can be provided to control the operation of structuredillumination imaging system 100, including synchronizing the variousoptical components of system 100. The controller can be implemented tocontrol aspects of system operation such as, for example, configurationof light structuring optical assembly 155 (e.g., selection and/or lineartranslation of diffraction gratings), movement of tube lens 156,focusing, stage movement, and imaging operations. The controller may bealso be implemented to control hardware elements of the system 100 tocorrect for changes in structured illumination parameters over time. Forexample, the controller may be configured to transmit control signals tomotors or other devices controlling a configuration of light structuringoptical assembly 155, motion stage 170, or some other element of system100 to correct or compensate for changes in structured illuminationphase, frequency, and/or orientation over time. In implementations,these signals may be transmitted in accordance with structuredillumination parameters predicted using SIM imaging component 191. Insome implementations, controller 195 may include a memory for storingpredicted and or estimated structured illumination parameterscorresponding to different times and/or sample positions.

In various implementations, the controller 195 can be implemented usinghardware, algorithms (e.g., machine executable instructions), or acombination of the foregoing. For example, in some implementations thecontroller can include one or more CPUs, GPUs, or processors withassociated memory. As another example, the controller can comprisehardware or other circuitry to control the operation, such as a computerprocessor and a non-transitory computer readable medium withmachine-readable instructions stored thereon. For example, thiscircuitry can include one or more of the following: field programmablegate array (FPGA), application specific integrated circuit (ASIC),programmable logic device (PLD), complex programmable logic device(CPLD), a programmable logic array (PLA), programmable array logic (PAL)and other similar processing device or circuitry. As yet anotherexample, the controller can comprise a combination of this circuitrywith one or more processors.

FIG. 3 is an optical diagram illustrating an example opticalconfiguration of a two-arm SIM imaging system 200 that may implementstructured illumination parameter prediction in accordance with someimplementations described herein. The first arm of system 200 includes alight emitter 210A, a first optical collimator 220A to collimate lightoutput by light emitter 210A, a diffraction grating 230A in a firstorientation with respect to the optical axis, a rotating mirror 240A,and a second optical collimator 250A. The second arm of system 200includes a light emitter 210B, a first optical collimator 220B tocollimate light output by light emitter 210B, a diffraction grating 230Bin a second orientation with respect to the optical axis, a rotatingmirror 240B, and a second optical collimator 250B. Although diffractiongratings are illustrated in this example, in other implementations,other beam splitting elements such as a beam splitter cube or plate maybe used to split light received at each arm of SIM imaging system 200.

Each light emitter 210A-210B may be an incoherent light emitter (e.g.,emit light beams output by one or more excitation diodes), or a coherentlight emitter such as emitter of light output by one or more lasers orlaser diodes. In the example of system 200, each light emitter 210A-210Bis an optical fiber that outputs an optical beam that is collimated by arespective collimator 220A-220B.

In some implementations, each optical fiber may be optically coupled toa corresponding light source (not shown) such as a laser. Duringimaging, each optical fiber may be switched on or off using a high-speedshutter (not shown) positioned in the optical path between the fiber andthe light source, or by pulsing the fiber's corresponding light sourceat a predetermined frequency during imaging. In some implementations,each optical fiber may be optically coupled to the same light source. Insuch implementations, a beam splitter or other suitable optical elementmay be used to guide light from the light source into each of theoptical fibers. In such examples, each optical fiber may be switched onor off using a high-speed shutter (not shown) positioned in the opticalpath between the fiber and beam splitter.

In example SIM imaging system 200, the first arm includes a fixedvertical grating 230A to project a structured illumination pattern or agrating pattern in a first orientation (e.g., a vertical fringe pattern)onto the sample, and the second arm includes a fixed horizontal grating230B to project a structured illumination pattern or a grating patternin a second orientation (e.g., a horizontal fringe pattern) onto thesample 271. The gratings of SIM imaging system 200 do not need to bemechanically rotated or translated, which may provide improved systemspeed, reliability, and repeatability.

In alternative implementations, gratings 230A and 230B may be mounted onrespective linear motion stages that may be translated to change theoptical path length (and thus the phase) of light emitted by gratings230A and 230B. The axis of motion of linear motion of the stages may beperpendicular or otherwise offset from the orientation of theirrespective grating to realize translation of the grating's pattern alonga sample 271.

Gratings 230A-230B may be transmissive diffraction gratings, including aplurality of diffracting elements (e.g., parallel slits or grooves)formed into a glass substrate or other suitable surface. The gratingsmay be implemented as phase gratings that provide a periodic variationof the refractive index of the grating material. The groove or featurespacing may be chosen to diffract light at suitable angles and tuned tothe minimum resolvable feature size of the imaged samples for operationof SIM imaging system 200. In other implementations, the gratings may bereflective diffraction gratings.

In the example of SIM imaging system 200, the vertical and horizontalpatterns are offset by about 90 degrees. In other implementations, otherorientations of the gratings may be used to create an offset of about 90degrees. For example, the gratings may be oriented such that theyproject images that are offset ±45 degrees from the x or y plane ofsample 271. The configuration of example SIM imaging system 200 may beparticularly advantageous in the case of a regularly patterned sample271 with features on a rectangular grid, as structured resolutionenhancement can be achieved using only two perpendicular gratings (e.g.,vertical grating and horizontal grating).

Gratings 230A-230B, in the example of system 200, are configured todiffract the input beams into a number of orders (e.g., 0 order, ±1orders, ±2 orders, etc.) of which the ±1 orders may be projected on thesample 271. As shown in this example, vertical grating 230A diffracts acollimated light beam into first order diffracted beams (±1 orders),spreading the first orders on the plane of the page, and horizontalgrating 230B diffracts a collimated light beam into first orderdiffracted beams, spreading the orders above and below the plane of thepage (i.e., in a plane perpendicular to the page). To improve efficiencyof the system, the zeroth order beams and all other higher order beams(i.e., ±2 orders or higher) may be blocked (i.e., filtered out of theillumination pattern projected on the sample 271). For example, a beamblocking element (not shown) such as an order filter may be insertedinto the optical path after each diffraction grating to block the0-order beam and the higher order beams. In some implementations,diffraction gratings 230A-230B may configured to diffract the beams intoonly the first orders and the 0-order (undiffracted beam) may be blockedby some beam blocking element.

Each arm includes an optical phase modulator or phase shifter 240A-240Bto phase shift the diffracted light output by each of gratings 230. Forexample, during structured imaging, the optical phase of each diffractedbeam may be shifted by some fraction (e.g., ½, ⅓, ¼, etc.) of the pitch(λ) of each fringe of the structured pattern. In the example of FIG. 3 ,phase modulators 240A and 240B are implemented as rotating windows thatmay use a galvanometer or other rotational actuator to rotate andmodulate the optical path-length of each diffracted beam. For example,window 240A may rotate about the vertical axis to shift the imageprojected by vertical grating 230A on sample 271 left or right, andwindow 240B may rotate about the horizontal axis to shift the imageprojected by horizontal grating 230B on sample 271 up or down.

In other implementations, other phase modulators that change the opticalpath length of the diffracted light (e.g. linear translation stages,wedges, etc.) may be used. Additionally, although optical phasemodulators 240A-240B are illustrated as being placed after gratings230A-230B, in other implementations they may be placed at otherlocations in the illumination system.

In alternative implementations, a single-phase modulator may be operatedin two different directions for the different fringe patterns, or asingle-phase modulator may use a single motion to adjust both pathlengths. For example, a large, rotating optical window may be placedafter mirror 260 with holes 261. In this case, the large window may beused in place of windows 240A and 240B to modulate the phases of bothsets of diffracted beams output by the vertical and horizontaldiffraction gratings. Instead of being parallel with respect to theoptical axis of one of the gratings, the axis of rotation for the largerotating window may be offset 45 degrees (or some other angular offset)from the optical axis of each of the vertical and horizontal gratings toallow for phase shifting along both directions along one common axis ofrotation of the large window. In some implementations, the largerotating window may be replaced by a wedged optic rotating about thenominal beam axis.

In example system 200, a mirror 260 with holes 261 combines the two armsinto the optical path in a lossless manner (e.g., without significantloss of optical power, other than a small absorption in the reflectivecoating). Mirror 260 can be located such that the diffracted orders fromeach of the gratings are spatially resolved, and the unwanted orders canbe blocked. Mirror 260 passes the first orders of light output by thefirst arm through holes 261. Mirror 260 reflects the first orders oflight output by the second arm. As such, the structured illuminationpattern may be switched from a vertical orientation (e.g., grating 230A)to a horizontal orientation (e.g., grating 230B) by turning each emitteron or off or by opening and closing an optical shutter that directs alight source's light through the fiber optic cable. In otherimplementations, the structured illumination pattern may be switched byusing an optical switch to change the arm that illuminates the sample.

Also illustrated in example imaging system 200 are a tube lens 265, asemi-reflective mirror 280, objective 270, and camera 290. For example,tube lens 265 may be implemented to articulate along the z-axis toadjust the structured beam shape and path. Semi-reflective mirror 280may be a dichroic mirror to reflect structured illumination lightreceived from each arm down into objective 270 for projection ontosample 271, and to pass through light emitted by sample 271 (e.g.,fluorescent light, which is emitted at different wavelengths than theexcitation) onto camera 290.

Output data (e.g., images) from camera 290 may be communicated to areal-time SIM imaging component (not shown) that may be implemented as asoftware application that, as further described below, may reconstructthe images captured during each imaging cycle to create an image havinga higher spatial resolution. The reconstructed images may take intoaccount changes in structure illumination parameters that are predictedover time. In addition, the real-time SIM imaging component may be usedto track predicted SIM parameters and/or make predictions of SIMparameters given prior estimated and/or predicted SIM parameters.

A controller (not shown) can be provided to control the operation ofstructured illumination imaging system 200, including synchronizing thevarious optical components of system 200. The controller can beimplemented to control aspects of system operation such as, for example,configuration of each optical arm (e.g., turning on/off each optical armduring capture of phase images, actuation of phase modulators240A-240B), movement of tube lens 265, stage movement (if any stage isused) of sample 271, and imaging operations. The controller may be alsobe implemented to control hardware elements of the system 200 to correctfor changes in structured illumination parameters over time. Forexample, the controller may be configured to transmit control signals todevices (e.g., phase modulators 240A-240B) controlling a configurationof each optical arm or some other element of system 100 to correct orcompensate for changes in structured illumination phase, frequency,and/or orientation over time. As another example, when gratings230A-230B are mounted on linear motion stages (e.g., instead of usingphase modulators 240A-240B), the controller may be configured to controlthe linear motion stages to correct or compensate for phase changes. Inimplementations, these signals may be transmitted in accordance withstructured illumination parameters predicted using a SIM imagingcomponent. In some implementations, the controller may include a memoryfor storing predicted and or estimated structured illuminationparameters corresponding to different times and/or sample positions.

It should be noted that, for the sake of simplicity, optical componentsof SIM imaging system 200 may have been omitted from the foregoingdiscussion. Additionally, although system 200 is illustrated in thisexample as a single channel system, in other implementations, it may beimplemented as a multi-channel system (e.g., by using two differentcameras and light sources that emit in two different wavelengths).

Although system 200 illustrates a two-arm structured illuminationimaging system that includes two gratings oriented at two differentangles, it should be noted that in other implementations, the technologydescribed herein may be implemented with systems using more than twoarms. In the case of a regularly patterned sample with features on arectangular grid, resolution enhancement can be achieved with only twoperpendicular angles (e.g., vertical grating and horizontal grating) asdescribed above. On the other hand, for image resolution enhancement inall directions for other samples (e.g., hexagonally patterned samples),three illumination peak angles may be used. For example, a three-armsystem may include three light emitters and three fixed diffractiongratings (one per arm), where each diffraction grating is orientedaround the optical axis of the system to project a respective patternorientation on the sample (e.g., a 0° pattern, a 120° pattern, or a 240°pattern). In such systems, additional mirrors with holes may be used tocombine the additional images of the additional gratings into the systemin a lossless manner. Alternatively, such systems may utilize one ormore polarizing beam splitters to combine the images of each of thegratings.

FIGS. 4A and 4B are schematic diagrams illustrating an example opticalconfiguration of a dual optical grating slide SIM imaging system 500that may implement structured illumination parameter prediction inaccordance with some implementations described herein. In example system500, all changes to the structured illumination pattern or the gratingpattern projected on sample 570 (e.g., pattern phase shifts orrotations) may be made by linearly translating a motion stage 530 alonga single axis of motion, to select a grating 531 or 532 (i.e., selectgrating orientation) or to phase shift one of gratings 531-532.

System 500 includes a light emitter 510 (e.g., optical fiber opticallycoupled to a light source), a first optical collimator 520 (e.g.,collimation lens) to collimate light output by light emitter 510, alinear motion stage 530 mounted with a first diffraction grating 531(e.g., horizontal grating) and a second diffraction grating 532 (e.g.vertical grating), a tube lens 540, a semi-reflective mirror 550 (e.g.,dichroic mirror), an objective 560, a sample 570, and a camera 580. Forsimplicity, optical components of SIM imaging system 500 may be omittedfrom FIG. 4A. Additionally, although system 500 is illustrated in thisexample as a single channel system, in other implementations, it may beimplemented as a multi-channel system (e.g., by using two differentcameras and light sources that emit in two different wavelengths).

As illustrated by FIG. 4A, a grating 531 (e.g., a horizontal diffractiongrating) may diffract a collimated light beam into first orderdiffracted light beams (on the plane of the page). As illustrated byFIG. 4B, a diffraction grating 532 (e.g., a vertical diffractiongrating) may diffract a beam into first orders (above and below theplane of the page). In this configuration only a single optical armhaving a single emitter 510 (e.g., optical fiber) and single linearmotion stage is needed to image a sample 570, which may provide systemadvantages such as reducing the number of moving system parts to improvespeed, complexity and cost. Additionally, in system 500, the absence ofa polarizer may provide the previously mentioned advantage of highoptical efficiency. The configuration of example SIM imaging system 200may be particularly advantageous in the case of a regularly patternedsample 570 with features on a rectangular grid, as structured resolutionenhancement can be achieved using only two perpendicular gratings (e.g.,vertical grating and horizontal grating).

To improve efficiency of the system, the zeroth order beams and allother higher order diffraction beams (i.e., ±2 orders or higher) outputby each grating may be blocked (i.e., filtered out of the illuminationpattern projected on the sample 570). For example, a beam blockingelement (not shown) such as an order filter may be inserted into theoptical path after motion stage 530. In some implementations,diffraction gratings 531-532 may configured to diffract the beams intoonly the first orders and the zeroth order (undiffracted beam) may beblocked by some beam blocking element.

In the example of system 500, the two gratings may be arranged about±45° from the axis of motion (or other some other angular offset fromthe axis of motion such as about +40°/−50°, about +30°/−60°, etc.) suchthat a phase shift may be realized for each grating 531 and 532 along asingle axis of linear motion. In some implementations, the two gratingsmay be combined into one physical optical element. For example, one sideof the physical optical element may have a structured illuminationpattern or a grating pattern in a first orientation, and an adjacentside of the physical optical element may have a structured illuminationpattern or a grating pattern in a second orientation orthogonal to thefirst orientation.

Single axis linear motion stage 530 may include one or more actuators toallow it to move along the X-axis relative to the sample plane, or alongthe Y-axis relative to the sample plane. During operation, linear motionstage 530 may provide sufficient travel (e.g., about 12-15 mm) andaccuracy (e.g., about less than 0.5 micrometer repeatability) to causeaccurate illumination patterns to be projected for efficient imagereconstruction. In implementations where motion stage 530 is utilized inan automated imaging system such as a fluorescence microscope, it may beconfigured to provide a high speed of operation, minimal vibrationgeneration and a long working lifetime. In implementations, linearmotion stage 530 may include crossed roller bearings, a linear motor, ahigh-accuracy linear encoder, and/or other components. For example,motion stage 530 may be implemented as a high-precision stepper or piezomotion stage that may be translated using a controller.

Output data (e.g., images) from camera 580 may be communicated to areal-time SIM imaging component (not shown) that may be implemented as asoftware application that, as further described below, may reconstructthe images captured during each imaging cycle to create an image havinga higher spatial resolution. The reconstructed images may take intoaccount changes in structure illumination parameters that are predictedover time. In addition, the real-time SIM imaging component may be usedto track predicted SIM parameters and/or make predictions of SIMparameters given prior estimated and/or predicted SIM parameters.

A controller (not shown) can be provided to control the operation ofstructured illumination imaging system 500, including synchronizing thevarious optical components of system 500. The controller can beimplemented to control aspects of system operation such as, for example,translation of linear motion stage 530, movement of tube lens 540, stagemovement (if any stage is used) of sample 570, and imaging operations.The controller may be also be implemented to control hardware elementsof the system 500 to correct for changes in structured illuminationparameters over time. For example, the controller may be configured totransmit control signals to devices (e.g., linear motion stage 530) tocorrect or compensate for changes in structured illumination phase,frequency, and/or orientation over time. In implementations, thesesignals may be transmitted in accordance with structured illuminationparameters predicted using a SIM imaging component. In someimplementations, the controller may include a memory for storingpredicted and or estimated structured illumination parameterscorresponding to different times and/or sample positions.

Although the example of FIGS. 4A-4B illustrates a dual optical gratingslide imaging system that may implement structured illuminationparameter prediction, structured illumination parameter prediction maybe implemented in SIM imaging systems that use a linear motion actuatormounted with more than two diffraction gratings.

Projection Parameter Estimation

Parameter estimation for SIM image processing may be needed to correctfor undesired changes in structured illumination parameters over time.By way of example, FIGS. 5A-5C illustrate undesired changes in frequencyor illumination peak spacing (FIG. 5A), phase or phase displacement(FIG. 5B), and orientation or illumination peak angle (FIG. 5C) that mayoccur over time in a SIM imaging system that projects a one-dimensionalstructured illumination pattern on a regularly patterned sample. Inparticular, FIG. 5A illustrates a sample 50 with features 51 illuminatedby a one-dimensional structured illumination pattern having fringes 60,before and after frequency shifts. Before any frequency shifts, adjacentfringes 60 have a pitch or center-to-center-spacing of P correspondingto an initial frequency f. Over time, with temperature variations in thesystem, the pitch P may increase or decrease. For example, thermalexpansion may cause the pitch P to increase to P+□P₁, correspondinglydecreasing the frequency f to f−□f₁. Conversely, thermal contraction maycause the pitch P to decrease to P−□P₁, correspondingly increasing thefrequency f to f+□f₂.

FIG. 5B illustrates the sample 50 illuminated by a one-dimensionalstructured illumination pattern having fringes 60, before and afterchanges in a phase. As shown, before phase drift, a first phase state Φmay correspond to fringes completely illuminating every second column offeatures 51 of sample 50. Over time, the position of the fringes 60relative to the sample 50 may shift such that all phase images areoffset by □Φ. For example, mechanical vibrations in the SIM imagingsystem (e.g., in an excitation beam path), imprecision in a translationstage used by a grating or sample stage, thermal variations, and/orother factors may cause an undesired drift in the phase. After the phasedrifts by □Φ, the first phase state changes to Φ+□ □Φ, and the fringesno longer are centered on every second column of features.

FIG. 5C illustrates the sample 50 illuminated by a one-dimensionalstructured illumination pattern having fringes 60, before and afterchanges in orientation. As shown, before a change in orientation, theorientation of the fringes relatively to sample 50 are completelyvertical. Over the time, the orientation may change due to factors suchas changes in the excitation beam path, movement of the sample, thermalvariations, and/or other factors. After the orientation rotates by anangle □θ, the fringes are no longer completely vertical relative to thesample.

Parameter estimation during SIM imaging process to precisely account forchanges in structured illumination parameters as described above helpsensure an artifact-free and accurate reconstruction of an image from aset of sampled images. However, such a process may be computationallyexpensive and is frequently performed after image acquisition. Fortime-critical SIM imaging systems that involve real-time processing andreconstruction of images, and thus real-time estimation of parameterssuch as frequency, phase, orientation, and modulation order, thesecomputational requirements may result in a loss of data throughput(e.g., less data may be processed per unit of time). In such systems,the rate at which samples are imaged may exceed the rate at whichstructured illumination parameters may be directly estimated from thesampled images. As such, there is a need for a method of generating aparameter estimate with low complexity and low processing time.

To this end, implementations of the technology disclosed herein aredirected to predicting structured illumination parameters for aparticular point in time, space, and/or temperature using estimates ofstructured illumination parameters obtained from images captured by thestructured illumination system. Particular implementations are directedto predicting structured illumination frequency, phase, orientation,and/or modulation order parameters.

In accordance with some implementations, a structured illuminationparameter may be predicted for a given point in time, space, and/ortemperature by interpolating estimates of the structured illuminationparameter from image captures. For example, a first frequency may beestimated from a first sampled image, a second frequency may beestimated from a second sampled image, and a frequency corresponding toa point in time between the first captured image and the second capturedimage (e.g., a frequency for an image taken between the first and secondimages) may be predicted by interpolating using at least a determinedrate of change of the frequency between the first captured image and thesecond captured image.

In accordance with some implementations, a structured illuminationparameter may be predicted for a given point in time, space, and/ortemperature by extrapolation using estimates of a structuredillumination parameter obtained from two image captures. For example, afirst orientation may be estimated from a first sampled image, a secondorientation may be estimated from a second sampled image, and anorientation corresponding to a point in time after the first and secondcaptured images (e.g., an orientation for a third image taken after thefirst and second images) may be predicted by extrapolation using atleast a determined rate of change of the orientation from the firstcaptured image to the second captured image. As a second example, afirst orientation may be estimated from a first sampled image, a secondorientation may be estimated from a second sampled image, and anorientation corresponding to a point in time after the first and secondcaptured images (e.g., an orientation for a third image taken after thefirst and second images) may be predicted by holding the value from thesecond captured image.

In implementations, estimated and predicted structured illuminationparameters may be used to narrow a search space for other structuredillumination parameters that are predicted. For example, given anestimated value of a structured illumination parameter for a first pointin time, space, and/or temperature, a value of the structuredillumination parameter for second point in time, space, and/ortemperature that is near the first point in time, space, and/ortemperature may be predicted taking into account the predicted orestimated value at the first point in time, space, and/or temperature.

In implementations, estimated and predicted structured illuminationparameters may be stored in a memory of the structured illuminationsystem for later use by the system. For instance, predicted andestimated parameters may be stored in a history file such as a lookuptable. Predicted parameters that are stored in memory may be determinedfrom estimated parameters, or they may be set based on the physicalcharacteristics of the structured illumination system. For example, thenominal grid spacing of the structured illumination system may bestored. The stored parameters may thereafter be referenced to performoperations such as: calibrated image reconstruction, providing feedbackto a hardware component to correct for changes in structuredillumination parameters, and narrowing the search space when predictingadditional structured illumination parameters.

Illumination Fringe Patterns

FIG. 6 illustrates simplified illumination fringe patterns that may beprojected onto the plane of a sample 271 by a vertical grating 230A andhorizontal grating 230B of SIM imaging system 200 during one imagingcycle to use structured light to create a high-resolution image. In thisexample, three phase images with a vertical illumination orientation maybe captured using vertical grating 230A, and three phase images with ahorizontal illumination orientation may be captured using horizontalgrating 230B. For each orientation, projected fringes may be phaseshifted in position in steps of ⅓λ (e.g., by setting phase modulator230A or 230B to three different positions) to capture three phase imagesof the orientation pattern.

During capture of each phase image, any light emitted by the sample maybe captured by camera 290. For instance, fluorescent dyes situated atdifferent features of the sample 271 may fluoresce and the resultantlight may be collected by the objective lens 270 and directed to animage sensor of camera 290 to detect the florescence. The captured siximages may be used to image an entire sample or a location of a largersample.

Once all images have been captured for the imaging cycle (in thisexample, six images), a high-resolution image may be constructed fromthe captured images. For example, a high-resolution image may bereconstructed from the six images shown in FIG. 6 . Suitable algorithmsmay be used to combine these various images to synthesize a single imageof the sample with significantly better spatial resolution than any ofthe individual component images.

During construction of the high-resolution image, undesired shifts orchanges in structured illumination parameters (e.g., angle, spacing andphase), may be algorithmically compensated for using structuredillumination parameters predicted in accordance with the disclosure(e.g., predicted changes in angle, spacing or phase). For example,offsets in the phases, angle, and/or spacing of the verticalillumination images and/or the horizontal illumination images may becompensated for.

In some implementations, undesired shifts or changes in structuredillumination parameters may be compensated for prior to image capture bycontrolling one or more hardware elements of system 200 to compensatefor those changes in the SIM imaging system. For example, prior to animaging sequence and/or in between capture of images of an imagingsequence, phase drift may be compensated for each optical arm byadjusting a phase shifting element (e.g., rotating mirror, linearactuator, etc.). In some implementations, a combination of hardware andalgorithmic compensation may be implemented.

FIG. 6 illustrates an implementation which patterned flow cell containsnanowells that are patterned symmetrically. In some implementations, thenanowells can be patterned asymmetrically, for example, the nanowell canbe shaped or configured to form an elongated structure. The elongatedstructure refers to a shape where the dimension along a first axis isgreater than the dimensions along a second axis. Asymmetrically shapednanowells can be elliptical, rectangular, etc. In such implementations,the sample can be resolved along the direction or axis of the longerside and SIM is used to increase resolution along the shorter side. Ifthe shorter side of nanowells is along x-axis and longer side is alongy-axis, the pitch Px of asymmetrically patterned flow cell is narrow ortight, entailing an increase in resolution, while along the y-axis, thepitch Py is larger. Accordingly, resolution is increased along x-axisand only three images are captured in order to adequately resolve asample contained within the nanowells of flow cell.

FIG. 7 illustrates simplified illumination fringe patterns that may beprojected onto the plane of a sample 570 by a first diffraction gratingand a second diffraction grating of a dual optical grating slide SIMimaging system 500 during image capture for a structured illuminationimaging cycle. For example, a SIM imaging system 500 may use a firstdiffraction grating 531 and second diffraction grating 532 to generatethe illumination patterns shown in FIG. 7 . As illustrated in theexample of FIG. 7 , the two gratings project perpendicular fringepatterns on the surface of sample 570 and are arranged about ±45° fromthe axis of motion of linear motion stage 530.

For example, a first grating (e.g., grating 531), may projectfirst-order illumination fringes on sample 570. Any light emitted by thesample may be captured by camera 580 and a first phase image of thefirst pattern (e.g., +45° pattern) may be captured to create a firstphase image. To capture additional phase shifted images, the patternprojected by the grating may be phase shifted by translating the linearmotion stage. These phase shift motions are illustrated as steps 1 and 2in FIG. 7 . The phase shift motions may provide small (e.g., about 3 to5 micrometers or smaller) moves of the gratings to slightly shift thefringe pattern projected on the grating.

Following capture of all phase shifted images for a diffraction grating,system 500 may switch diffraction gratings by translating the linearmotion stage 530 to optically couple another diffraction grating to thelight source of the imaging system (e.g., transition from FIG. 4A to4B). This motion is illustrated as step 3 in the example of FIG. 7 . Inthe case of diffraction grating changes, the linear motion stage mayprovide a relatively large translation (e.g., on the order of 12-15 mm).

A series of phase images may then be captured for the next grating. Forinstance, as illustrated by FIG. 7 , a second diffraction grating mayproject first-order illumination fringes on the sample, and theprojected fringes may be shifted in position by translating the linearmotion stage 530 to capture three phase images of the grating's pattern(e.g., steps 4 and 5 of FIG. 7 ).

Once all images have been captured for the imaging cycle (in thisexample, six images), a high-resolution image may be constructed fromthe captured images. For example, a high-resolution image may bereconstructed from the six images shown in FIG. 7 . Suitable algorithmsmay be used to combine these various images to synthesize a single imageof the sample with significantly better spatial resolution than any ofthe individual component images.

During construction of the high-resolution image, undesired shifts orchanges in structured illumination parameters (e.g., angle, spacing andphase), may be algorithmically compensated for using structuredillumination parameters estimated in accordance with the disclosure(e.g., predicted changes in angle, spacing or phase). For example,offsets in the phases, angle, and/or spacing of the verticalillumination images and/or the horizontal illumination images may becompensated for. In one implementation, one or more of the offsets inphases, angle and/or spacing of the images can be calculatedperiodically such as in the first cycle of a sequencing run. Thefrequency of these estimations can be increased or decreased based onthe environmental factors that impact the stability of the process.

In some implementations, undesired shifts or changes in structuredillumination parameters may be compensated for prior to image capture bycontrolling one or more hardware elements of system 500 to compensatefor those changes in the SIM imaging system. For example, prior to animaging sequence and/or in between capture of images of an imagingsequence, phase drift may be compensated for by translating linearmotion stage 530. In some implementations, a combination of hardware andalgorithmic compensation may be implemented.

In accordance with implementations described herein, structuredillumination parameters may be predicted for a particular point in timeusing estimates of structured illumination parameters obtained fromimages captured before and/or after that point in time. For example,computational resource limitations may limit the rate at which a SIMimaging system (e.g., system 100, 200, or 500) may directly estimatestructured illumination parameters such as phase, frequency, and/ororientation from captured images. In some cases, a SIM imaging systemmay directly estimate or measure a structured illumination parameterevery phase image, in which case it may not be necessary to predictstructured illumination parameters. However, in other cases, a SIMimaging system may only be able to directly estimate or measure astructured illumination parameter for some phase images of an imagingcycle, once per imaging cycle, or even less frequently (e.g., every 3,5, 10, 50, or 100 imaging cycles). In such cases, to keep up with theimage sampling rate of the system, it may be advantageous to leverage adirect estimate of the structured illumination parameter that wasobtained for a particular point in time and/or space to make predictionsabout the structured illumination parameter at other points in timeand/or space.

To mathematically illustrate one example of this principal, correlationagainst a reference is one way to estimate structured illuminationparameters.

Correlation Output=Σ_(r) c(x)h(x−f)  (1)

where h(x) is a reference which may either be known or derived fromimage data, c(x) is derived from image data which is correlated to thereference, and f is a value to be estimated (in this example,frequency). It should be noted that other alternative estimationtechniques may be utilized in accordance with the disclosure.

In the example of Equation (1), one correlation output may be generatedfor each of a number of hypothetical values of f. The parameter estimatef□ may be obtained as the value of f which maximizes the magnitude ofthe correlation. However, in many cases, a large number of hypotheticalvalues off may need to be attempted in order to maximize the correlationoutput. The large search space may increase the computationalrequirements, and as a result, may cause reduced system throughput(i.e., less data processed per unit of time).

Optical Sensing (Especially Lens) Distortion

In the following two sections we present techniques to estimateparameters for SIM image reconstruction. Some of the techniquesdisclosed compensate for fringe peak lines that are distorted or bentdue lens imperfections. Pattern lines that are supposed to be parallelbegin that way near the center of the image but tend to converge orbecome non-parallel near the edge of the lens. This impacts illuminationpeak angle or orientation, illumination peak spacing, and phase offset.In FIG. 8A, we illustrate dividing an image tile into overlappingregions referred to as subtiles or subwindows or subfields. The subtilesare small enough that parameters can be set that will give satisfactoryreconstruction for a whole subtile. The parameter estimation can beperformed in two steps. First, parameter estimation can be performed fora near-center subtile of the image. Then, parameter estimation can beperformed for other subtiles and compared to the near-center subtile todetermine distortions and corrections for the distortions, relative toparameters for the near-center subtile.

To illustrate an implementation of these techniques, we present examplepseudocode for parameter estimation in a “subtile angle and spacingmapping” algorithm (also referred to as Algorithm 2A), and “subtilephase mapping” algorithm (also referred to as Algorithm 2B). We alsopresent an example pseudocode to determine distortions and correctionsfor the distortions in estimated parameters for non-center subtiles whencompared with near-center subtiles in “final distortion calibrationalgorithm” (also referred to as Algorithm 1).

FIGS. 8A to 8C illustrate physical aspects of the full field of view(FOV). In one implementation, the rectangular sensor is used that is5472 pixels by 3694 pixels. Of course, a square sensor or a differentsize of sensor could be used, for example, 5472×5472 pixels, or4800×4800 pixels. When a rectangular sensor is used, distortion isgreatest closest to the edge of the lens. A lens often is round, so arectangular sensor does not come as close to the edge of the lens on thelong side as it does on the short side.

FIG. 8A presents two illustrations that show fringe spacing distortionacross the full field of view (FOV). The FIG. 801 on the left is asimplified depiction 801 of bending parallel lines due to distortion ofa lens that magnifies. The lines depicted are intended to be parallel inthe image plane. Viewed through a lens, they appear to converge at rightand left ends, relative to spacing in the center. The FIG. 805 on theright is another exaggerated example. In this figure the fringe linesare oriented diagonally between top left and bottom right corners. Thefringe spacing is exaggerated to make it easier to see. The fringe linesconverge at the top left and bottom right corners, relative to thecenter. For a particular manufacturer's lens, the fringe pattern can benon-uniform.

FIGS. 8B and 8C depict measurements of spacing in an image betweennominally parallel fringe peaks in the image plane, for green and bluelaser illumination. The color scale indicates a variation in spacingbetween 2.08 and 2.22 (sensor pixels). In both drawings, the color scaleindicates that the center spacing between parallel lines isapproximately 2.14. Irregularity under green wavelength illumination isseen in the top right-hand corner of FIG. 8B. More substantialirregularity under blue wavelength illumination is seen in FIG. 8C,along the right and left edges. In these figures, the fringe pattern isa series of parallel lines, from top left to bottom right of thefigures. Thus, the spacing is measured normal to the general directionof arrow in FIG. 8C. These figures motivate correction of distortionscaused by the lens. Since lenses are individually manufactured andmounted, calibration and correction of individual systems after assemblyis desirable.

The fringe pattern is a series of parallel lines at an angle of 45°,from bottom left to top right of the figure. The spacing between theparallel lines increases in the center region as illustrated by alighter shade of gray. As we move away from the center region to topright or bottom left regions, the spacing distortion between the fringelines decreases. The major axes of the three contour lines areapproximately normal to the direction of the fringe lines. The spacingdistortion decreases as we move out from region in the center contourline.

FIG. 8D illustrates subtiles or subfields of the full field of view(FOV) in an image tile. In this figure, the subtile illustrated is 512pixels by 512 pixels. These subtiles may subdivide the field of vision,shown, or may overlap. Subtiles may be larger or smaller. For instance,400×400 and 1024×1024 pixel subtiles have been shown to be workable. Thefigure illustrates 5×7 subtiles. The larger sensor called out above mayhave 8×11 subtiles. Other configurations of subtiles such as 3×3, 5×5,5×7, 9×9, 9×16 can be used. Larger sensors can be divided into moresubtiles. The subtiles can overlap by at least 2 pixels of the opticalsensor. Larger and smaller number of pixels can be used for overlappingbetween subtiles. For example, for a 512-pixel wide subtile, up to a256-pixel overlap can be used, and for a 1024-pixel wide subtile, up toa 256-pixel overlap can be used. Consistent with FIGS. 8B and 8C, thereare several candidate near-center subtiles 803, all in the sweet spot ofthe lens, including a center subtile in an odd x odd subtile array. Asused herein, a near-center subtile either includes a center pixel of thesensor or abuts a subtile that includes the center pixel. In someoptical systems that are flat and have small error, a subtile furtherfrom the ones adjoining the center subtile can be used as a referencewithout impacting the overall distortion compensation.

The technology disclosed includes mapping distortion measured oversubstantially the full field of view captured by the sensor. Threeparameters on which enhanced resolution SIM reconstruction fromregularly structured illumination depend include fringe spacing, fringeangle, and phase displacement of the fringe pattern. These variables arealso referred to as spacing, angle and phase offset of the structuredillumination or grating pattern. The spacing and angle deviations fromthe center tile value can be fit across the full field of view usingpolynomial surfaces. Both quadratic and cubic surfaces have beeninvestigated. Higher order polynomials also could be used.

Both the fringe spacing and fringe angle across the image tile can befit by quadratic surfaces. Sensitivity analysis shows that quadraticsurfaces fit very nearly as well as cubic surfaces. A quadratic surfaceis fit to the equation:

f(x,y)=c0+(c1*x)+(c2*y)+(c3*x*y)+(c4*x ²)+(c5*y ²)  (2)

In later cycles of the imaging, we can characterize the contraction orexpansion of the spacing from center subtile due to temperature changesand apply the same deviation correction to the rest of the image.Depending on the contraction and expansion of near center subtile, thedeviation correction can be scaled and translated accordingly. We haveobserved the relative deviations in subtiles are constant with respectto the center or the near center subtile that is used as a reference.

FIG. 9 depicts a color-coded surface that was fitted to measured datapoints indicated in red. As this surface is three-dimensional, referenceto the color version of the figure is recommended to understand thecontours. The USPTO, at time of filing, saves the color version to thesupplemental content tab of the PAIR online system, where it becomespublicly available in due course.

In practice, it has been observed that spacing distortion across thefield of view can be represented as a bowl or inverted bowl shape whileangle distortion characteristically takes on the saddle shape. FIGS. 10Aand 10B illustrate the inverted bowl shape of measured spacing, relativeto spacing in a near-center subtile. FIGS. 12A and 12B illustrate thesaddle shape of measured angles, relative to a fringe pattern angle inthe near-center subtile.

In FIGS. 10A and 10B, the x-axis is along the length of the sensor. They-axis is along the width of the sensor. The labels on x-axis and y-axisare subtile indices. The vertical axis spacing is normalized to 1.000spacing on the z-axis between regularly spaced fringes viewed in thenear-center subtile. FIG. 10A depicts measured data points, not smoothedby curve fitting. It shows approximately 1.2 percent variation inobserved fringe spacing between measurements in the center and the lowerright portions of the sensor. In FIG. 10B, the actual data points,depicted in blue, are compared to a quadratically fitted surface. In thefollowing section, we present measuring spacing and angle distortionsacross the full FOV as compared with near-center subtile.

Calibration of Spacing and Angle Distortion

In this section, we further describe techniques to calibrate a fullfield of view (FOV) over a sensor applied to capture of structuredillumination and to provide fit a function to non-linear effects causedby lens distortion. Structured illumination involves projecting aregularly spaced and angled pattern onto an image plane. An image tileis captured of the projected structured illumination pattern. Anear-center subtile is selected from the full field of view. Patternspacing and angle over the subtile are measured, including variationwithin the subtile. We further determine the spacing and pattern angleacross other subtiles over the full field of view. We express thespacing and pattern angle across the full field of view relative to thespacing and pattern angle in the near-center subtile. We repeatedly fitthe polynomial surfaces to the spacing and the pattern angle oversubstantially the full field of view.

FIGS. 11A, 11B and 11C compare measured spacing distortion withquadratic and cubic surface fits. Color variation is subtle in the threefigures, because there is less than 2% variation in spacing over thefield of view in all three figures. Visual analysis of these figures andcalculation of fitting errors suggest that a quadratic polynomialprovides a sufficiently good fit for spacing distortion. The spirit andteaching of this disclosure cover higher order polynomials andalternative fitting schemes that may produce marginally better or worseresults than a quadratic polynomial.

FIGS. 12A and 12B share the x and y axes configuration with the FIGS.10A and 10B. The vertical axis in these figures indicates an angle oforientation of fringes observed over the field of view. Nominally, theangle might have been intended to be 45°. The range of measured anglesis from 44.85 to 45.15 degrees, which is a variation of 0.6%. FIG. 12Adepicts measured data points, not smoothed by curve fitting. In FIG.12B, the actual data points, depicted in blue, are compared to aquadratically fitted surface.

FIGS. 13A to 13E and 14B to 14H illustrate how cropping along the sensorborder can improve curve fitting for spacing and fringe angle over thesensor. The x and y axes of graphs in these figures represent pixelspositions along the length and width of the sensor. Referring to FIG.8C, cropping is indicated by a border near edges of the sensor. Thefigure illustrates that this cropping or shrinkage removes thedistortion near the edges of the image captured by the sensor. Thefollowing figures illustrate how this shrinking of the image enables agood fit of the FOV data to the surface model. Shrink factor or a unitof shrinkage can be equal to shrinking the image by one sliding windowstride. A stride value can be chosen to match the size of subtiles orchosen empirically based on number of pixels at the edges of the sensorthat can cause distortion. For example, if the stride value is chosen as200 pixels then one unit of shrinkage can remove the outer 200 pixels ofimage data that are of poor data quality on all sides of the image.Stride values smaller than 200 pixels such as 25, 50 or 100 pixels orlarger than 200 pixels such as 225, 250, or 300 pixels can be used.

FIG. 13A shows the quadratic model surface for spacing distortion fit tothe image data for full field of view (FOV), without cropping, in bluedots. No shrinking factor is applied to this full FOV image data i.e.,the shrink factor is zero. As illustrated in FIG. 13A, the full FOV datadoes not provide a good fit for the model. As the shrink factor isincreased incrementally from 1 to 4 (FIGS. 13B to 13E), the fit betweenthe model and the uncropped data points improves. As cropping eliminatesoutlying data points, the scale along z-axis shrinks, indicating lessvariation among subtiles. Referring back to FIG. 8C, one sees that theoutlier data points are along the edges of sensor, where cropping isapplied. In FIGS. 13C, many more outlying blue data points are apparentthan in 13D-E. In FIG. 13E, the MSE has dropped to 0.000001 and thez-scale has stretched a little lower than in FIG. 13D.

FIGS. 14B to 14H illustrate improvement in quadratic surface fit forangle distortion by incrementally increasing the shrink factor appliedto the full FOV image data. As the shrink factor increases, the scalealong the z-axis again shrinks, bringing the body of data points closeto the fitted surface. The exclusion of outlying, distorted values onthe edges of the sensor greatly improves the surface fit. In FIGS.14B-E, the distortion is evident from the plane blue data points belowthe fitted surface. When the shrink factor is increased from “1” (FIG.14C) to “3” (FIG. 14E), the scale along the z-axis shrinks in half from[−45, −20] to [−44, −40]. In FIG. 14F, cropping finally appears to bringthe data points and the fitted surface closer together. In FIG. 14F theMSE drops to 0.000033. The quadratic surface model for angle distortionnow fits matches the measure data shown as blue dots. In FIG. 14H,increasing the shrink factor is “6”, produces a marginal improvement.From the figures, a shrink factor of “4” or more appears to be in order.A threshold based on percentage or absolute MSE improvement can be usedto select a shrink factor or the impact of shrink factors on resolvingpower can be determined. Selection of a shrink factor can be automatedusing a ratioing or thresholding approach.

Characterizing the imaging system by fitting distortion surfaces can beperformed before and/or during operation of the instrument. byoccasionally re-estimating the spacing and pattern angle in thenear-center subtile and using the extrapolation factors to determine thespacing and the pattern angle across the full field of view. Theextrapolation factors can be applied without estimating spacing andpattern angle beyond the near-center subtile. The subtiles of the fullfield of view can overlap. During calibration, the spacing and thepattern angle are calculated independently for the overlapping subtiles.This approach can use two angles of orientation for the structuredillumination pattern such that the two angles are substantially alignedwith opposing corners of a rectangular or quadrilateral pattern ofnanowells.

Thus, the technology disclosed includes calibrating and re-calibratingan instrument to characterized variation among subtiles of a tile.Characteristic distortion of spacing and angles can be captured in a waythat greatly reduces the need to parameter estimation during instrumentoperation. The characterization/calibration can be enhanced by croppingaway pixels at the edge of a sensor that are so distorted as not to fita mapping surface, cropping away pixels that interfere with fitting.

Application of Calibrated Spacing and Angle

The technology disclosed can efficiently reconstruct an enhanced imageacross substantially a full field of view. For this purpose, we dividethe full field of view into at least nine subtiles or subfields forindependent reconstruction on a subtile basis. We can estimate or accessalready estimated spacing parameters and pattern angle parameters fromviewing a regularly spaced and angled structured illumination patternfor a near-center subtile. We apply extrapolation factors to extend thenear-center subtile parameters to other subtiles across the field ofview. We use the near-center subtile estimated parameters and the othersubtiles' extrapolated parameters to perform enhanced imagereconstruction from multiple images. The multiple images are viewed atmultiple pattern angles and periodically displaced pattern phases ofrespective pattern angles by applying structured illumination microscopy(SIM) analysis to the multiple images. In one implementation, thetechnology described above can further estimate the periodicallydisplaced pattern of phases of the respective pattern angles. Thetechnology described above can be extended to stitching together thesubtiles, by identifying a regular pattern of noise along stitchingborders, and mitigating the noise.

FIGS. 15A and 15B are examples of distortion models fitted from realdata collected from sequencing instruments. FIG. 15A illustratesquadratic fit response surface obtained by fitting the fringe spacingdistortion across the full field of view (FOV). FIG. 15B illustratesquadratic fit response surface obtained by fitting the fringe angledistortion across the FOV.

Estimation of Phase Displacement

Phase displacement is the remaining, third input parameter for SIM imagereconstruction. This section adapts existing techniques to estimate thephase displacement. The technology disclosed performs estimation onsubtiles, so we further present a technique to establish a common frameof reference for estimated phase displacement across subtiles, withrespect to the full FOV tile. One implementation of phase estimationadapts the technique proposed by Wicker et al. 2013, in their papertitled, “Phase optimisation for structured illumination microscopy”,section 3. Equations from Lal et al. 2015 titled, “Structuredillumination microscopy image reconstruction algorithm,” and from Wickeret. al. 2013 help explain Wicker phase estimation.

Equation (3) below, taken from Lal et al. 2015 separates three bands offrequency components: {tilde over (S)}(k) {tilde over (H)}(k); {tildeover (S)}(k−p_(θ)) {tilde over (H)}(k); {tilde over (S)}(k+p_(θ)) {tildeover (H)}(k), from acquired images {tilde over (D)}_(θ,ϕ) ₁ (k), {tildeover (D)}_(θ,ϕ) ₂ (k), {tilde over (D)}_(θ,ϕ) ₃ (k). The mixing matrixuses estimates of the phases ϕ₁, ϕ₂, and, ϕ₃ of images captured using asinusoidal illumination intensity pattern I_(θ,Φ)(r), corresponding to apattern angle or orientation θ. Wicker et. al. 2013 refer to phase forn^(th) image at an orientation as ϕ_(n). If phases are not known withsufficient precision, the unmixing or band separation process willimperfectly separate the spatial frequency components from the observedimages {tilde over (D)}_(θ,ϕ) ₁ (k), {tilde over (D)}_(θ,ϕ) ₂ (k),{tilde over (D)}_(θ,ϕ) ₃ (k) in frequency domain. Practically, the threespatial frequency components {tilde over (S)}(k) {tilde over (H)}(k);{tilde over (S)}(k−p_(θ)) {tilde over (H)}(k); {tilde over (S)}(k+p_(θ)){tilde over (H)}(k) will contain more or less residual information fromother components, as represented by the noise term.

$\begin{matrix}{\begin{bmatrix}{{\overset{\sim}{D}}_{\theta,\phi_{1}}(k)} \\{{\overset{\sim}{D}}_{\theta,\phi_{2}}(k)} \\{{\overset{\sim}{D}}_{\theta,\phi_{3}}(k)}\end{bmatrix} = {{\frac{I_{o}}{2}{M\begin{bmatrix}{{\overset{\sim}{S}(k)}{\overset{\sim}{H}(k)}} \\{{\overset{\sim}{S}\left( {k - p_{\theta}} \right)}{\overset{\sim}{H}(k)}} \\{{\overset{\sim}{S}\left( {k + p_{\theta}} \right)}{\overset{\sim}{H}(k)}}\end{bmatrix}}} + \begin{bmatrix}{{\overset{\sim}{N}}_{\theta,\phi_{1}}(k)} \\{{\overset{\sim}{N}}_{\theta,\phi_{2}}(k)} \\{{\overset{\sim}{N}}_{\theta,\phi_{3}}(k)}\end{bmatrix}}} & (3)\end{matrix}$ ${{where}M} = \begin{bmatrix}1 & {{- \frac{m}{2}}e^{{- i}\phi_{1}}} & {{- \frac{m}{2}}e^{{+ i}\phi_{1}}} \\1 & {{- \frac{m}{2}}e^{{- i}\phi_{2}}} & {{- \frac{m}{2}}e^{{+ i}\phi_{2}}} \\1 & {{- \frac{m}{2}}e^{{- i}\phi_{3}}} & {{- \frac{m}{2}}e^{{+ i}\phi_{3}}}\end{bmatrix}$

This formulation with three components follows from the Fouriertransform for sine or cosine illumination. A different illuminationfunction would change the equations.

Precise knowledge of the illuminating sinusoidal intensity patternphases is therefore important. As it is not always possible to preciselycontrol these phases in experimental setup, it is desirable to determinethe illumination pattern phases from the acquired image data. Wicker et.al. 2013 present a phase estimation technique for SIM data acquiredusing coherent sinusoidal illumination at a selected frequency. Coherentillumination produces good pattern contrast from fine gratings with avery small illumination peak spacing ‘s’, which enhances thereconstructed resolution. We retrieve illumination pattern phase of then^(th) image using the illumination pattern's peak frequency. Theillumination pattern's peak frequency is also referred to as Fourierpeak.

Equation (4) below from Wicker et. al. 2013 presents a generalized formof equation (3) with acquired images {tilde over (D)}_(n)({right arrowover (k)}) over frequencies {right arrow over (k)} in the frequencydomain. Each image comprises of three components that are referred to as{tilde over (C)}⁻¹({right arrow over (k)}), {tilde over (C)}₀({rightarrow over (k)}), {tilde over (C)}₊₁({right arrow over (k)})superimposed with different phases. Note that these three components arethe same three components as {tilde over (S)}(k) {tilde over (H)}(k);{tilde over (S)}(k−p_(θ)) {tilde over (H)}(k); {tilde over (S)}(k+p_(θ)){tilde over (H)}(k) in equation (4).

$\begin{matrix}\begin{matrix}{{{\overset{\sim}{D}}_{n}\left( \overset{\rightarrow}{k} \right)} = {{e^{{- \iota}\phi_{n}}{\overset{˜}{C}}_{- 1}\left( \overset{\rightarrow}{k} \right)} + {{\overset{˜}{C}}_{0}\left( \overset{\rightarrow}{k} \right)} + {e^{\iota\phi_{n}}{\overset{˜}{C}}_{+ 1}\left( \overset{\rightarrow}{k} \right)}}} \\{= {{\frac{c}{2}e^{{- \iota}\phi_{n}}\overset{˜}{S}\left( {\overset{\rightarrow}{k} + \overset{\rightarrow}{p}} \right){\overset{\sim}{h}\left( \overset{\rightarrow}{k} \right)}} + {\overset{˜}{S}\left( \overset{\rightarrow}{k} \right){\overset{\sim}{h}\left( \overset{\rightarrow}{k} \right)}} + {\frac{c}{2}e^{\iota\phi_{n}}\overset{˜}{S}\left( {\overset{\rightarrow}{k} - \overset{\rightarrow}{p}} \right){\overset{\sim}{h}\left( \overset{\rightarrow}{k} \right)}}}}\end{matrix} & (4)\end{matrix}$

Note that ‘c’ in equation (4) is referred to as contrast of theillumination pattern. In the absence of noise, ‘c’ is the same as themodulation factor ‘m’ in mixing matrix M in equation (3). To determineϕ_(n), the frequency {right arrow over (k)} in equation (4) is replacedwith {right arrow over (p)} which is peak frequency of illuminationpattern, resulting in equation (5).

$\begin{matrix}{{\phi_{n} \approx {\arg\left\{ {{\overset{\sim}{D}}_{n}\left( \overset{\rightarrow}{p} \right)} \right\}}} = {\arg\left\{ {{\frac{c}{2}e^{{- \iota}\phi_{n}}{\overset{˜}{S}\left( {2\overset{\rightarrow}{p}} \right)}{\overset{\sim}{h}\left( \overset{\rightarrow}{p} \right)}} + {{\overset{˜}{S}\left( \overset{\rightarrow}{p} \right)}{\overset{\sim}{h}\left( \overset{\rightarrow}{p} \right)}} + {\frac{c}{2}e^{\iota\phi_{n}}{\overset{˜}{S}(0)}{\overset{\sim}{h}\left( \overset{\rightarrow}{p} \right)}}} \right\}}} & (5)\end{matrix}$

Equation (5) shows that pattern phase ϕ_(n) is approximately equal tothe phase of the acquired image {tilde over (D)}_(n)({right arrow over(p)}) over frequency {right arrow over (p)}. This approximate estimationof the pattern phase ϕ_(n) yields good results when three guidelines arefollowed. First, the contrast c of the illumination pattern should to besufficiently large. Second, the sample power spectrum should decreasesufficiently fast with growing frequency. When these two guidelines arefollowed, equation (5) is dominated by the last term and therefore, canbe simplified to:

ϕ_(n)≈arg{e ^(ιϕ) ^(n) {tilde over (S)}(0){tilde over (h)}({right arrowover (p)})}  (6)

For any real valued sample, the center frequency {tilde over (S)}(0)will be real valued. Further, if the point spread function (PSF)h({right arrow over (r)}) is real and symmetrical, the optical transferfunction (OTF) {tilde over (h)}({right arrow over (k)}) will be real. AnOTF is a convolution of the point spread function (PSF). A point spreadfunction is the spatial domain version of the optical transfer functionof the imaging system. The name “point spread function” indicates thatall physical optical systems blur (spread) a point of light to somedegree, with the amount of blurring being determined by the quality ofthe optical components. The resolution of the imaging system is limitedby the size of the PSF. For asymmetrical PSFs the phases of the OTFs,have to be taken into account.

Third, the OTF at the pattern frequency {tilde over (h)}({right arrowover (p)}) should be sufficiently large to overcome noise. If the OTF istoo small, noise in the acquired image can significantly alter the phasemeasured at {right arrow over (p)}. This phase estimation method cannotbe used for pattern frequencies {right arrow over (p)} outside for thesupport of the detection OTF. For such frequencies, {tilde over(h)}({right arrow over (p)})=0.

An optical system's OTF can be determined experimentally. For example,Lal et al. 2015 compute the OTF by obtaining several images of sampleswith sparsely distributed 100 nm fluorescent microspheres. Intensitydistribution corresponding to more than 100 microspheres were thensuperimposed and averaged to obtain an approximation for the system PSF.Fourier transform of this PSF provides an estimate of system OTF. Withthis background, we can apply the phase estimation technique describedto subtiles.

Translating Subtile Phases to a Common Frame of Reference

It is useful to estimate phase displacement of tiles relative to thefull field of view (FOV), so that measurement of phase in one subtilecan be extrapolated to other subtiles across the tile. The illuminationpeak angle and illumination peak spacing for the full FOV can beestimated from the illumination peak angle and illumination peak spacingof the subtile using the quadratic models presented above. The phasedisplacement is less regular because it depends pixel geometry ofsubtiles, which can produce an irregular step function, instead of asmooth function. Each phase estimate has a “frame of reference” anchoredto the top-left corner of the image being used to estimate the phase. Asa result, when we want to correct for phase differences geometricallyacross the image, the phases estimated from each subtile need to becompared to a reference phase (from the center estimation subwindow orsubtile) in a common frame of reference. We translate the phase estimateof each subtile to the same geometric location to achieve the commonframe of reference. Translating the phase requires an accurate estimateof the fringe angle and spacing for each subtile. The fringe angle andspacing can be obtained from quadratic surface distortion models thatwere calibrated as a result of the distortion calibration process.

We represent phase estimates using a common frame of reference acrosssubtiles of the full FOV image. Subtile coordinate spaces are mapped toa the full FOV coordinate space. In this section, we illustratetranslation of point coordinates from one space to another. FIG. 16Aillustrates a general setup of a subtile window in a full FOV imagewindow and relevant variables and a corrected phase offset formula thattranslates coordinates between local and global references. FIG. 16Ashows a boundary 1603 representing the full field of view which containsa subtile shown by a boundary 1605. The crop angle α is defined by aline 1606 joining the top left corner of the full FOV window 1603 andthe top left corner of the subtile window 1605. The length of the line1606 is measured in pixels and is labeled “a”.

An illumination peak angle β is formed between the top border of thesensor, window 1603, and a line 1607 perpendicular to a fringe peak line1609 at the top left corner of subtile window 1605. This fringe peakline will be parallel to actual fringe peaks and likely to be offsetfrom the peaks. The length of the line 1607 from the top left corner of1603 to 1609 is a projected distance “b”. The phase difference ϕ,between top left corners of the tile 1603 and the subtile 1605 is afunction of spacing s and distance b. In formula (7), the illuminationpeak spacing is represented as “s” in pixels and the phase in degrees:

ϕ_(o)=([b+(ϕ_(s)/360*s)]% s)/s*360  (7)

This formula, derived and explained below, can be applied to translate aphase offset from subtile coordinates to full FOV coordinates.Translation between coordinate systems is illustrated in two examplesthat follow.

In SIM reconstruction a one-dimensional (1D) grating can be described byan intensity function such as:

$\begin{matrix}{{I(x)} = {M\sin\left( {{\frac{2\pi}{s}\left\langle {u,x} \right\rangle} + \phi} \right)}} & (8)\end{matrix}$

where the parameters have the following meaning:

-   -   M is the modulation depth    -   s is the illumination phase spacing

$u = {\begin{pmatrix}{\cos\beta} \\{\sin\beta}\end{pmatrix}{is}a{unit}{vector}}$

-   -   β is the illumination peak angle

The bold-faced x, u in the equation represent vectors: x is a vector inthe image space representing a position in the image space at which theintensity is determined; u is a unit vector. The intensity values of thesinusoidal illumination are positive, in a scaled range from a +M(bright) to −M (dark).

Once the phase displacement difference between subtiles has beencharacterized, only the phase displacement at the near-center subtilesneeds to be estimated, because phase displacements of the other subtilescan be extrapolated from the near-center subtile.

The following calculations are related to the estimation of the phaseoffset ϕ. Rather than estimating the phase offset using the data of anentire image (full FOV), it is possible to use the data from only asubtile of the image. The phase offset of the subtile is related to, butnot identical to, the phase offset of the full FOV. In order to recoverthe phase offset of the full FOV from the subtile phase offset, it isnecessary to characterize how the phase offset transforms under aspatial translation. The following mathematical model presents thisspatial translation from subtile to full FOV.

Derivation of Coordinate Transformation

Consider the function below that generates grating lines at anorientation as shown in FIG. 16A. The two-dimensional (2D) space of thefull FOV is represented by coordinates space O_(x) having coordinates(x₁, x₂) as shown in FIG. 16B. We represent a point 1611 by a position(x₁, x₂) with respect to O_(x). A function θ(x₁, x₂) can be definedbelow as shown in equation (9). This function can be used to generatethe illumination pattern lines at an orientation as shown in FIG. 16A.

$\begin{matrix}{{f\left( {x_{1},x_{2}} \right)} = {\sin\left( \frac{x_{1} - x_{2}}{\sqrt{2}} \right)}} & (9)\end{matrix}$

The one-dimensional periodicity of the above function ƒ is representedby function in equation (10). Referring to FIG. 16A, this functionrepresents the repetition of the illumination pattern lines as we movealong the line 1607 in FIG. 16A which is perpendicular to theillumination pattern lines. This function can determine the spacing “s”between the two lines by determining the position at which a particularintensity value is repeated. Equation (10) shows periodicity of theintensity of the illumination pattern. In Equation (10), the line normalto intensity peaks is at a 45 degree angle extending down and to theright from the top left corner, so intensity at the point (x₁, x₂) isrepeated at a successive point (x₁+√{square root over (2)}π, x₂−√{squareroot over (2)}π) down and to the right along the line 1607.

ƒ(x ₁+√{square root over (2)}π,x ₂−√{square root over (2)}π)=ƒ(x ₁ ,x₂)  (10)

The illumination peak spacing “s” defined by the above function is shownin equation (11). For convenience, the coordinate system can be scaledto match the spacing of the illumination pattern, as shown in Equation(11).

s=∥(√{square root over (2)}π,−√{square root over (2)}π)∥=2π  (11)

The illumination peak angle is defined by equation (12). Theillumination peak angle is formed between the top border (horizontalline) of the full FOV and a line perpendicular to the illuminationpattern lines as shown in FIG. 16A. Equation (12) shows this angle is−45 degrees for the structured illumination pattern generated by the 1Dperiodicity function in equation (9).

$\begin{matrix}{\beta = {{\arctan\left( \frac{\sqrt{2}\pi}{{- \sqrt{2}}\pi} \right)} = {{\arctan\left( {- 1} \right)} = {- \frac{\pi}{4}}}}} & (12)\end{matrix}$

The zero set of the pattern lines formed by the periodicity functionpresented in equation (9) is a union of lines and is represented byequation (13).

Z(ƒ)=

L _(n)

L _(n) ={x∈

² :x ₂ =x ₁+2√{square root over (2)}πn}  (13)

The zero set includes the structured illumination pattern lines spacedat a distance “s” from each other. The pattern lines are formed by thepoints at which value of the 1D function ƒ(x₁, x₂) in equation (9) iszero. A unit vector that is orthogonal to the grating is defined byequation (14).

$\begin{matrix}{u = {\left( {{\cos\beta},\ {\sin\beta}} \right) = \left( {\frac{1}{\sqrt{2}},{- \frac{1}{\sqrt{2}}}} \right)}} & (14)\end{matrix}$

We now present two examples of translating the phase offset of a subtilefrom the subtile coordinate space O_(y) defined by the coordinates (y₁,y₂) to the full FOV coordinate space O_(x). FIG. 16B illustrates a point1611 which can be represented in the subtile coordinates by a vector{right arrow over (y)} originating from the top left corner of thesubtile window 1605. The same point 1611 can be represented in the fullFOV coordinates by a vector x originating from the top left window 1603for the full FOV image. A vector {right arrow over (a)} connects the topleft corner of the full FOV image window 1603 to the subtile window 1605as shown in FIG. 16B. In the following sections we present examples tothe position of the point 1611 can be translated from one coordinatesystem to another coordinate system.

First Example

Let a=(a, −a)∈

², represent the upper left corner of the subtile 1605 in FIG. 16B. Itmeans that the top left corner of the subtile 1605 is displaced from thetop left corner of the full FOV window 1603 by a distance ‘a’ alonghorizontal axis x₁ and by a distance ‘−a’ along the vertical axis x₂. Wenow translate a point (x₁, x₂) in the full FOV coordinate space O_(x) toa point (y₁, y₂) in the subtile coordinate space ϕ_(y). Consider thechange of coordinates given as:

(y ₁ ,y ₂)=T(x ₁ ,x ₂)=(x ₁ −a,x ₂ +a)  (15)

We can now represent the 1D periodicity function of equation (10) in thesubtile coordinate space Oy as:

$\begin{matrix}{{g\left( {y_{1},y_{2}} \right)} = {{f\left( {T^{- 1}\left( {y_{1},y_{2}} \right)} \right)} = {{f\left( {{y_{1} + a},{y_{2} - a}} \right)} = {\sin\left( {\frac{y_{1} - y_{2}}{\sqrt{2}} + {\sqrt{2}a}} \right)}}}} & (16)\end{matrix}$

We can see that the phase offset introduced by the change of coordinatesis given as:

ϕ_(a)=√{square root over (2)}a=∥a∥  (17)

Second Example

Now let us consider another position of the subtile with respect to thetop left corner of the full FOV window. Let a=(2a, −a)∈

², in this case the change of coordinates given as:

(y ₁ ,y ₂)=T(x ₁ ,x ₂)=(x ₁−2a,x ₂ +a)  (18)

The 1D periodicity function of equation (10) can now be represented inthe subtile coordinate space Oy as:

$\begin{matrix}{{g\left( {y_{1},y_{2}} \right)} = {{f\left( {T^{- 1}\left( {y_{1},y_{2}} \right)} \right)} = {{f\left( {{y_{1} + {2a}},{y_{2} - a}} \right)} = {\sin\left( {\frac{y_{1} - y_{2}}{\sqrt{2}} + \frac{3a}{\sqrt{2}}} \right)}}}} & (19)\end{matrix}$

In this case, the phase offset introduced by the change of coordinatesis given as:

$\begin{matrix}{\phi_{a} = {\frac{3a}{\sqrt{2}} = \left\langle {u,a} \right\rangle}} & (20)\end{matrix}$

whereas

u, a

represents dot product of vector u with vector a. The dot product isdefined as the sum of the corresponding components of the two vectors.

General Case

Theorem 1.

Let s>0, ϕ₀∈

, and let u∈

² be a unit vector. Consider the function

$\begin{matrix}{{f(x)} = {\sin\left( {{\frac{2\pi}{s}\left\langle {u,x} \right\rangle} + \phi_{0}} \right)}} & (21)\end{matrix}$

a) The grating spacing of ƒ is s.b) For any a∈

² there exists ϕ_(a)∈

such that

$\begin{matrix}{{f\left( {x - a} \right)} = {\sin\left( {{\frac{2\pi}{s}\left\langle {u,x} \right\rangle} + \phi_{a}} \right)}} & (22)\end{matrix}$

Equation (21) illustrates the periodicity function with respect to thefull FOV coordinate space Ox and has a phase offset ϕ₀. Equation (22)shows the periodicity function for the subtile coordinate space and hasa phase offset ϕ₀.

c) Any such ϕ_(a) satisfies the equation

$\begin{matrix}{\phi_{0} \equiv {{\frac{2\pi}{s}\left\langle {u,a} \right\rangle} + {\phi_{a}{mod}\ 2{\pi.}}}} & (23)\end{matrix}$

Proof (a) The illumination peak spacing is the length of a fundamentalperiod for ƒ. The vector su is a period of length s, since

$\begin{matrix}{{f\left( {x + {su}} \right)} = {\sin\left( {{\frac{2\pi}{s}\left\langle {u,\ {x + {su}}} \right\rangle} + \phi_{0}} \right)}} & (24)\end{matrix}$ $\begin{matrix}{{f\left( {x + {su}} \right)} = {\sin\left( {{\frac{2\pi}{s}\left\langle {u,x} \right\rangle} + {2\pi\left\langle {u,u} \right\rangle} + \phi_{0}} \right)}} & (25)\end{matrix}$ $\begin{matrix}{{f\left( {x + {su}} \right)} = {f(x)}} & (26)\end{matrix}$

Any smaller period would imply a period of less than 2π for sin.(b) One solution is

$\begin{matrix}{\phi_{a} = {{{- \frac{2\pi}{s}}\left\langle {u,a} \right\rangle} + \phi_{0}}} & (27)\end{matrix}$

because

$\begin{matrix}{{f\left( {x - a} \right)} = {\sin\left( {{\frac{2\pi}{s}\left\langle {u,{x - a}} \right\rangle} + \phi_{0}} \right)}} & (28)\end{matrix}$ $\begin{matrix}{{f\left( {x - a} \right)} = {\sin\left( {{\frac{2\pi}{s}\left\langle {u,x} \right\rangle} - {\frac{2\pi}{s}\left\langle {u,a} \right\rangle} + \phi_{0}} \right)}} & (29)\end{matrix}$c) If sin(t−a)=sin(t−b) for all t∈

then a≡b mod 2π  (30)

The relationships derived above can be used to translate the phasedisplacement estimated in a first coordinate space to a secondcoordinate space, such as between a subtile and a full FOV image tile.This is useful, because phase displacement is also subject to distortionbetween the near-center subtile and the rim of a lens. In the followingsection, we present a phase bias look up table that can capture therelationship between phase displacement values for non-center subtileswith respect to center subtiles.

Estimation of Phase Bias

The technology disclosed includes estimating phase for each subtile andthen translating the phase offset to the full FOV coordinates, so thatestimated phase displacement values are represented in a common frame ofreference. We store the difference between the phase displacement valuesfor the non-center subtiles and the near-center subtile in a phase biasor phase difference lookup table for each angle of the illuminationpattern. In some cases, impact of environmental factors can increaseinstability of the system. In such cases, the phase bias lookup table isgenerated more frequently to compensate for increased systeminstability. For example, the phase bias table can be generated in firstcycle of every sequencing run. The frequency of generation of phase biaslookup table can be increased or decreased according to the stability ofthe system.

FIG. 17 presents an example phase bias lookup table 1701 with colorcoding of phase differences from a near-center subtile at 3, 5, whichhas a zero value. The lookup table 1701 contains the same number ofcells as the number of subtiles in the full FOV image data, 7×9 insubtiles in this example. The position of the cell in the table 1701 cancorrespond to position of the subtile in the full FOV image data. Thephase lookup table stores the differences in phase between each of thesubtile relative to the center window (3, 5) estimated phase. Theexample table contains phase bias for one angle of the structuredillumination. A similar phase bias table will be stored for each angleof the structured illumination pattern. The phase values are sensitiveto cumulative errors in estimated values of other parameters (angle andspacing). Note that the phase values are wrapped from 0 degree to 360degrees. The phase of the center subtile is shown as 0 degree in theexample table. The color coding indicates the degree of differences inthe phases of a subtiles corresponding to the center subtile. Estimatingthe phase bias requires fringe angle, spacing, phase of the centersubtile or subwindow, and the distortion model (which we assume is notchanging). In the following sections, we present example algorithms forestimating angle, spacing, and phase parameters.

In the above sections, we have described techniques for estimatingparameters for subtiles in the full FOV image. In pseudocode thatfollows, we further illustrate estimating the angle, spacing, and phaseparameters for subtiles. An example pseudocode for estimating angle andspacing parameters is presented below and is referred to as “subtileangle and spacing mapping” (also referred to as Algorithm 2A). We use aquadratic or cubic polynomial fit to estimate distortion coefficientsfor angle and spacing parameters for subtiles. An example pseudocodeimplementing the phase estimation technique is presented below and isreferred to as “subtile phase mapping” algorithm (also referred to asAlgorithm 2B). Estimates of the phase offsets can be stored in a phasebias look-up table for each angle or orientation of the illuminationpattern. In one implementation, a separate lookup table can be used foreach sequencing tile on the flowcell to account for minute differencesin flowcell orientation, surface defects and other non-idealities thatcan skew the estimated results. The pseudocode for an example algorithmfor final distortion calibration is presented last and is referred to as“final distortion calibration with quadratic fit and look up table”(also referred to as Algorithm 1 and Algorithm 2C).

Algorithm 1: Final Distortion Calibration with Quadratic Fit

In the first pseudocode, we can apply cropping to remove distortionsnear the edges of the image. This enables a good fit of the full FOVdata to the quadratic surface models for angle and spacing estimationsduring calibration/characterization.

-   -   Input: Collection of images, e.g. for 2 angles and 3 phases per        angle    -   Output: Distortion model coefficients    -   Algorithm:        -   1. Build the following images/filters (sized to match the            subregion)            -   a. OTF        -   2. Divide the image into equally sized, but overlapping            subregions, e.g. 512×512 using a sliding window method        -   3. Estimate parameters at the center of the image using,            e.g. 512×512 window (see subtile angle and spacing mapping            algorithm and subtile phase mapping algorithm below)            -   a. Estimate illumination peak angle and spacing for each                angle            -   b. Estimate modulation for each angle            -   c. Estimate phase for each image        -   4. For each subregion            -   a. Perform step 3 parameter estimation            -   b. Save subtile center x,y coordinate, fringe angle (per                angle), spacing (per angle) and phase 0 (per angle) of                the wicker phase estimate into arrays (2d arrays)        -   5. For fringe spacing and angle            -   a. Subtract the table value in step 4 by the center                window estimated values from step 3.            -   b. Perform optimal shrinkage estimation of the quadratic                surface                -   i. Fit a quadratic surface via least squares                    regression to the functional form                    f(x,y)=a+b*x²+c*y²+d*xy+e*x+f*y for each array (call                    this Z, for example, fringe spacing from angle 0) in                    Step 4 (using the same input array X, and Y, which                    correspond to the subregion center x,y coordinates).                -   ii. Evaluate the R² coefficient of determination                    from step i.                -   iii. Shrink the X, Y, Z array (removing the outer                    edge elements), and repeat step (i) until N number                    of shrinks have been performed (N=3-5 and is a                    tunable parameter)                -   iv. As the array is shrunk towards the center                    portion of the data, the fit quality is expected to                    improve, we select the coefficients at the elbow                    point (minimize shrink amounts while also maximizing                    R²) where the model fit is sufficiently good                    (current criteria is defined as less than 5%                    relative improvement in R² value.                -    Other acceptance criteria that could be used here                    include:                -    The improvement to mean squared error of fit (the                    next iteration of mean squared error is smaller than                    the current iteration of mean squared error by some                    pre-defined threshold                -    The improvement to the maximum fitted error of the                    quadratic surface (each successive iteration should                    reduce this error and increase the fit, if we detect                    that the successive improvements are plateauing                -    Regularized measure of error, such as MAE (mean                    absolute error) instead of MSE (mean squared error),                    using similar improvement thresholding as the                    previous two criteria.        -   6. The optimal coefficients from step 5 are the output of            the final distortion calibration algorithm.

Algorithm 2A: Subtile Angle and Spacing Mapping

Pseudocode for an implementation of illumination peak angle and spacingestimation for subtiles, used in steps 3-4 above and during operation,follows:

-   -   Input: Collection of images for the same angle such that the        phases of each image evenly spread out from 0 to 360 degrees    -   Output:        -   1. Location of peak in Fourier space (or Spacing and angle            of grating)        -   2. Modulation of the Fourier peak        -   3. Phase offset of first image    -   Algorithm:        -   1. Fast Fourier Transform of each image        -   2. Perform band separation        -   3. Take two component images:            -   a. The 0 (wide field) and +1 (high-resolution part)            -   b. Mask out all Fourier components of each image that do                no overlap (also known as common region)            -   c. Mask out low frequency components around DC and                around estimated fringe peak            -   d. Apply inverted OTF        -   4. Repeat for finer and finer grid around start point            -   For each point in a 10×10 grid evenly sampling some                range                -   i. Apply phase shift to one of the two images phases                -   ii. Multiply the one image with the conjugate of the                    other                -   iii. Sum the result                -   iv. Normalize with respect to the sum of the wide                    field masked Fourier image                -   v. Save the shift the provides the maximum of the                    absolute value of the normalized complex correlation                -    1. The absolute value of this number is the                    modulation                -    2. The angle of this complex number is the phase                    offset

Algorithm 2B: Subtile Phase Mapping

Pseudocode for an example implementation of phase shift estimation forsubtiles follows. The technology disclosed can use most recent bestestimates of spacing and angle parameter values for this mapping. Thesystem can also obtain estimated spacing and angle parameter valuesusing the distortion model and the center subwindow or subtileparameters.

-   -   Input:        -   1. Collection of images for a single angle        -   2. illumination peak spacing and angle estimated from            previously described algorithm    -   Output: Phase offset for each image in collection    -   Algorithm:        -   1. Perform Fast Fourier Transform (FFT) of each image        -   2. Create copy of FFT image        -   3. Apply OTF to each image        -   4. Shift one image        -   5. Multiply both images using conjugate and sum absolute            value of each complex number        -   6. Phase is angle of resulting complex number

Algorithm 2C: Phase Bias Lookup Learning

For phase bias, the following steps are carried out for every tile onthe flow-cell during the first cycle imaging of the sequencing run

-   -   Input: Collection of images, e.g. for 2 angles and 3 phases per        angle from each tile of the sequencing flowcell, a calibrated        distortion model from Algorithm 1    -   Output: Phase bias lookup table    -   Algorithm:        -   For each sequencing tile (not to be confused by SIM            reconstruction subtile)            -   i. Apply Step 3 of Algorithm 1 to estimate the center                window SIM parameters (spacing, angle, the phases of                each angle-channel pair) of the tile            -   ii. Divide the image into subtiles, For each subtile:                -   a. Apply distortion model via Algorithm 2A to obtain                    the subtile fringe spacing and angle for the subtile                -   b. Perform wicker phase estimate on the first phase                    image of each channel-angle pair using the given                    fringe parameters from step a. on the local subtile                -   c. Compute the difference between the phase obtained                    in Step iib and the center window phase 0 obtained                    from step i and save the phase bias into the                    corresponding 2d array for phase bias            -   iii. Store the completed phase bias lookup table in                memory with tile ID as the entry to be used later        -   Repeat step 1. for every other sequencing tile encountered            in the entire flowcell.    -   Note: For performance reasons, we estimate the phase bias lookup        for each tile in first cycle of the sequencing run, however, the        learning frequency can also be adjusted to learn on every N        cycle if there are stability concerns.

In these pseudocodes, we presented techniques to estimate and mapillumination peak angle, illumination peak spacing, and phasedisplacement parameters. The estimated parameters are used as input tostructured illumination microscopy (SIM) image reconstruction. In thefollowing section, we present a proposed SIM image reconstructionalgorithm that can efficiently reconstruct high-resolution images ofsubtiles and then combine those subtile images to reconstruction thehigh-resolution image of the full FOV.

SIM Image Reconstruction

The SIM image reconstruction technology disclosed uses the angle,spacing, and phase estimations of the subtiles to reconstruct ahigh-resolution image. The technology disclosed can construct a full FOVhigh-resolution image in two steps. First, a high-resolution SIM imageis reconstructed for a subtile using the subtile parameters. Second, thehigh-resolution images for the subtiles can be stitched together toconstruct the full FOV image. Alternatively, the high-resolutionsubtiles could be processed without stitching. This section begins withan example pseudocode for SIM image reconstruction of a subtile. This isreferred to as “subtile reconstruction” algorithm. The technologydisclosed exploits symmetries in the Fourier space by using thenon-redundant data in the frequency domain. This reduces thecomputations required for some core operations nearly by one half. Thissection also presents an example pseudocode for combining the SIM imagesfor subtiles to reconstruct the high-resolution image for the fullimage. This is referred to as “full field of view reconstruction”algorithm. Note that the parameter estimation techniques disclosed abovecan be used with traditional redundant or the disclosed non-redundantSIM image reconstruction techniques.

Algorithm 3: Subtile Reconstruction

Pseudocode for one implementation of reconstruct a high-resolutionstructured illumination microscopy image subtile follows.

-   -   Input:        -   a. A collection of images of the sample with some grating            profile (I_(angle,phase)). For each angle, we need at least            3 phase images, spaced out uniformly from 0 to 360 deg        -   b Parameters:            -   i. Illumination peak spacing and angle for group of                images corresponding to an angle            -   ii. Phase for each image    -   Output: One high-resolution reconstructed image for a subtile    -   Algorithm:        -   1. Preprocess images (e.g. high pass filter)        -   2. Create a large Fourier image buffer (its size will depend            on the final upsample factor required) and initialize to 0        -   3. For each angle            -   a. For set of images each with a unique phase for the                grating for given angle                -   i. Perform a Fast Fourier Transform on each image            -   b. Perform band separation on the collection of images                (n>=3 input images→3 output or component images)                -   i. Two of the three output images contain higher                    resolution information.                -   ii. The left-over image has all the standard                    information (original resolution image)                -   iii. Need to know phase for each image and                    modulation for entire set            -   c. Apply MTF to each of the component images            -   d. Shift each component image from band separation to                their proper location in the larger Fourier image buffer                and sum the components.        -   4. Build Wiener filter from input MTF by shift and summing a            2D MTF image into a large Fourier image            -   a. We do this for each of the input images for each                angle            -   b. This does not have to be done for each                reconstruction, it could be done once and provided as an                input parameter        -   5. Apply Wiener and Apodization filters in Fourier space to            the large Fourier representation of the reconstructed image        -   6. Perform an inverse Fast Fourier Transform on the large            Fourier image buffer to get the high-resolution image

More description of this non-redundant subtile reconstruction pseudocodeis provided than for other pseudocode, because the technology disclosedcannot be explained by reference to prior art. The technology disclosedexploits symmetries in the Fourier space to increase efficiency in theSIM image reconstruction and decrease computing load. FIG. 18 presents ahigh level over view of our non-redundant subtile constructiontechnology. Three images 1811, 1813, 1815 are acquired for each subtileper angle, preferably with uniformly spaced phases. We apply a discreteFast Fourier Transform (FFT) to convert the image data from spatialdomain (real space) to frequency domain (complex space). We exploit theFFT symmetry of the data representing the three images in the frequencydomain. We improve the efficiency of reconstructing the high-resolutionSIM image of the subtile by operating on the non-redundant portions ofthe image data in the frequency domain. The details of processing stepsof the non-redundant subtile reconstruction algorithm are presentedbelow.

Inputs

1-a. Collection of Images

The images of samples divided into subtiles are given as input to thesubtile reconstruction algorithm. The images are divided into subtilesor subwindows or subregions of a predefined size e.g., 512×512 pixels.Subtiles of other sizes can be used.

Collection of image tiles extends prior technologies because so manytiles are needed to capture and image plane. In one implementation,image tiles are acquired with a diffraction grating that produces asinusoidal illumination intensity patternI_(θ,Φ(r). The intensity varies according to location (x, y), represented by a vector r, which is a two-dimensional spatial position vector. For comparison, Lal et al.)2015 present a basic structured illumination microscopy reconstructionalgorithm which is labeled as “Algorithm 1 SIM-RA”. To explain the stepsof our SIM image reconstruction algorithm we have used mathematicalformulations from Lal et al. 2015. Note that the reconstructionalgorithm presented by Lal et al. 2015 is applied to a full image, whileour subtile reconstruction technology reconstructs a high-resolution SIMimage subtiles.

A mathematical formulation for I_(θ,Φ)(r) is presented in Equation (1)of Lal et al. 2015. For each orientation of the sinusoidal illuminationpattern θ, three SIM images of the specimen are acquired correspondingto three different illumination phases. In one embodiment, two sets ofimages are acquired using two angles of orientation of the sinusoidalillumination pattern i.e., θ₁=45°, and θ₂=135° respectively. The angleof orientation θ is also referred to as illumination peak angle β. Insuch an embodiment the nanowells can be positioned at the corners of asquare or, more generally, a rectangle or quadrilateral. The angles oforientation are selected such that the images are taken along the linesconnecting the opposing corners of the rectangle. This increases theapparent distance between nanowells.

In another implementation, three sets of images can be taken at threeangles of orientation for example, θ₁=0°, θ₂=60°, θ₃=120° or θ₁=30°,θ₂=90°, θ₃=150°. In this embodiment, the nanowells can be arranged atthe opposing corners of a hexagon. The images are taken along the linesconnecting the opposing ends of the hexagon geometry. In general, thesets of images can be selected to match the number of diagonalsconnecting opposing ends of the geometry in which the nanowells arearranged.

For each orientation θ, at least three images, spaced out from 0° to360°, are acquired corresponding to three different illumination phasesϕ₁=0°, ϕ₂=120°, and ϕ₃=240°. Uniform phase spacing is preferred. Moreimages, such as five, six or seven, can be acquired by using a smallerphase step.

1-b (i) Structured Illumination Pattern Angle and Spacing Parameters

Illumination peak spacing ‘s’ which defines the periodicity of thegrating or the illumination of the sinusoidal intensity patternI_(θ,Φ)(r) is also given as input. The illumination peak spacing ‘s’ andillumination peak angle θ for subtiles are estimated using the “subtileangle and spacing mapping” algorithm presented above (also referred toas Algorithm 2A). The “final distortion calibration, with quadratic fitand lookup table” algorithm (also referred to as Algorithm 1) is appliedto compensate errors in estimation of angle and spacing parameters fornon-center subtiles due to optical sensing distortions. Examplepseudocode for “subtile angle and spacing mapping” and “final distortioncalibration, with quadratic fit and lookup table” algorithms arepresented above.

1-b (ii) Phase Displacement Parameter

Phase displacement (also referred to as grating phase) ‘ϕ’ for thestructured illumination intensity pattern I_(θ,Φ)(r) is also given asinput. The phase values for the subtiles are estimated using “subtilephase mapping” algorithm (also referred to as Algorithm 2B). The “finaldistortion calibration, with quadratic fit and lookup table” algorithm(also referred to as Algorithm 1) is applied to compensate errors inestimation of phase parameter for non-center subtiles due to opticalsensing distortions. The difference between phase of non-center subtilesand the center subtile per angle are stored in a lookup table. Examplepseudocode for “subtile phase mapping” algorithm is presented above.With these inputs described, we turn to processing.

Processing Step 1: Preprocess Images (High Pass Filter)

Processing for subtile reconstruction can begin with imagepreprocessing. Pseudocode that generally corresponds to this descriptionfollows the explanation. The reconstruction of an enhanced image isperformed in the Fourier space. But before applying Fourier transform totake the image data from spatial domain to frequency domain, we canapply a preprocessing step as described below to image tiles. Thedescriptions that follow are based on three images with different phasevalues along one angle. Two or more orientations will be used, asdescribed above.

Acquired images can be preprocessed to remove noise. Noise has manysources. For example, exposure to intense excitation light can producephotobleaching that reduces the intensity of emission from thefluorophores. An illustration of noise in acquired SIM images is shownin FIG. 6 of Lal et al. 2015. The top left image FIG. 6 , labeled (a),shows a raw SIM image with background fluorescence blur. This introduceslarge errors in the frequency content of raw SIM images in a smallneighborhood around zero or DC frequency. A highpass filter, such as aGaussian highpass or Butterworth highpass filter, can be used topreprocess the raw SIM images. The second image in FIG. 6 , labeled as(b), shows a raw SIM image after background subtraction. The Fourierspectrum of both images (a) and (b) is the same as is shown. Fordetailed discussion on highpass filters the reader is referred chapter 4of Gonzales and Woods 2008, “Digital Image Processing”, 3^(rd) edition.In Chapter 4, Gonzales and Woods present an illustration in FIG. 4.52that includes perspective plots, image representations andcross-sections of ideal, Butterworth and Gaussian highpass filters.

Processing Step 2: Create a Large Fourier Image Buffer

A buffer in memory can be reserved and initialized to receive theenhanced Fourier image. The size of the memory buffer depends on theupsample factor, the degree of enhancement. The SIM image reconstructiontechnique can theoretically produce an enhanced resolution along eachangle or orientation of three images, that has up to 2 times theresolution of the acquired images along the orientation. For two orthree sets of three images, we can reserve a buffer to store thehigh-resolution image with an upsample factor of 2.

Processing Step 3: Loop steps 3-a to 3-d for each angle θ

Processing steps 3-a to 3-d are performed in a loop. In one iteration,the processing steps are performed for three images of a subtileacquired per angle. We now describe the details of the processing stepsin the loop.

Step 3-a (Fast Fourier Transform):

A discrete fast Fourier transform (FFT) is applied to a set of imagesacquired with the same angle or orientation ‘0’. As described above, weacquire at least three images with different phases, along eachorientation. Each image is transformed from the spatial domain to thefrequency domain. For a N×N image, the order of complexity ofcalculating a 2-D discrete Fourier transform (DFT) is O(N⁴), which canbe reduced to O(N³) by exploiting common subexpressions from row to rowand column to column. A further speed up can be achieved if N is a powerof 2. A Fast Fourier Transform (FFT) algorithm can achieve an order ofcomplexity of O(N² log₂ N) for an N×N image when N is a power of 2. Anexample of exploiting common expressions in rows and columns is throughthe use of “butterfly computations”. For details see Heckbert 1995,“Fourier Transforms and the Fast Fourier Transform (FFT) Algorithm”available athttp://www.cs.cmu.edu/afs/andrew/scs/cs/15-463/2001/pub/www/notes/fourier/fourier.pdf.Thus, choice of a subtile size, such as 512×512 pixels, trades offfrequency resolution against increased computation time. Subtiles with aside of 128, 256, 512, 1024, 2048, 4096 or in a range of 128 to 4096pixels can be used.

Image data in the spatial domain is represented as real numbers. When weperform the Fourier transform, the resulting data in the frequencydomain is represented as complex numbers. A complex number z can berepresented as z=x+iy where x and y are its real and imaginary parts.The conjugate of the complex number z is represented as z*=x−iy.

Two example corner-shifted Fourier domain matrices are shown in FIG. 19, depicting matrices with even (1910) and odd (1920) values of N, toillustrate the symmetries in the Fourier space. These images in thefrequency domain are represented by N×N matrices. The top row and leftcolumn of both matrices 1910 and 1920 are referred to as the DC row andDC column, corresponding to the zero or DC frequency components. Incontrast to these corner-shifted matrices of values, plots in thefrequency domain typically show the zero or DC frequency at the centerof a circle.

The matrices in FIG. 19 exhibit symmetries in the Fourier or frequencydomain. The DC row and DC column in matrices 1910 and 1920 haveleft-right and up-down conjugate symmetries. The matrix 1910, with aneven number of rows and columns, has a middle column that also hasup-down conjugate symmetry. Setting aside the top row and left column(1910, 1920) and, in the even matrix, the middle column (1910), theremaining portions of each matrix have 180-degree rotation conjugatesymmetry. These portions of the two 1910 and 1920 are shown in boldlines.

Dividing the full matrix in the frequency domain into two halves, aftersetting aside a row and column(s), produces a right half that, after180-degree rotation, is the conjugate of the left half. FIG. 19illustrates half matrices (1911, 1912) and (1921, 1922) for even and odddimensioned matrices (1910, 1920). Values for the right half can belooked up from data for the left half. Taking this look-up into account,the left half matrix is the so-called non-redundant data and the righthalf matrix is the redundant data. The number of columns in the lefthalf (slightly larger than a half) is ((N/2)+1) and the columns in theright half is (N−(N/2)−1). The technology disclosed keeps track ofsymmetries to reduce computing load.

When reconstructing the SIM images, we operate on the non-redundant lefthalf of the full image matrices in Fourier space. This increases theefficiency as number of computations required is reduced considerably.In the following SIM reconstruction steps, we will present furtherdetails about how the symmetries in the Fourier space are exploited.

Step 3-b (Band Separation):

Shifted and unshifted frequency components are present in each capturedimage, because the positions of sources are randomly distributedrelative to fringe peaks of illumination. This step separates shiftedand unshifted frequency components in the frequency domainrepresentation of the acquired images using a band separation matrixM⁻¹. The band separation matrix is also referred to as an inverse mixingmatrix (Wicker et. al. 2013) or a reconstruction matrix (Lal et al.2015). Gustafsson et. al. 2000 in their paper titled, “Doubling thelateral resolution of wide-field fluorescence microscopy usingstructured illumination” illustrate the band separation process in FIG.5 of the paper. The figure illustrates separated frequency componentsfrom observed images. The band separation can be understood fromequation (31) below, taken from Lal et al. 2015. The equation shows thatthe three components {tilde over (S)}(k) {tilde over (H)}(k); {tildeover (S)}(k−p_(θ)) {tilde over (H)}(k); {tilde over (S)}(k+p_(θ)) {tildeover (H)}(k) are separated from the three observed images on the rightside of the equation. The three images are acquired using an angle θ ofthe illumination pattern.

$\begin{matrix}{\begin{bmatrix}{{\overset{˜}{S}(k)}{\overset{\sim}{H}(k)}} \\{{\overset{˜}{S}\left( {k - p_{\theta}} \right)}{\overset{\sim}{H}(k)}} \\{{\overset{˜}{S}\left( {k + p_{\theta}} \right)}{\overset{\sim}{H}(k)}}\end{bmatrix} = {M^{- 1}\begin{bmatrix}{{\overset{\sim}{D}}_{\theta,\phi_{1}}(k)} \\{{\overset{\sim}{D}}_{\theta,\phi_{2}}(k)} \\{{\overset{\sim}{D}}_{\theta,\phi_{3}}(k)}\end{bmatrix}}} & (31)\end{matrix}$

The first component retrieved from equation (31) is the so-called DCcomponent {tilde over (S)}(k) {tilde over (H)}(k). This the componentwith unshifted spatial frequencies. When separated using precise phaseand modulation parameters, this component does not include highfrequency content beyond the Abbe diffraction limit. The second and thethird components {tilde over (S)}(k−p_(θ)) {tilde over (H)}(k); {tildeover (S)}(k+p_(θ)) {tilde over (H)}(k) contain high frequency contentshifted into the band pass region of the Abbe diffraction limit.

The band separation step leverages the disclosed non-redundant SIM imagereconstruction technology, as shown in FIGS. 20-22 . Suppose the fullmatrix of each subtile's image in Fourier space has 512 rows and 512columns as shown FIG. 20 . We separate matrix positions in the threefrequency domain matrices corresponding to the three images acquired atthree phases for one angle into non-redundant and redundant halves asexplained above. Each non-redundant half matrix has 512 rows and 257columns, including two columns and one row of DC components anddominated by non-redundant coefficients that have complementaryconjugate coefficients in the redundant half matrix. We reshape eachnon-redundant left half matrix into a vector and combine the threevectors for three half matrixes into a matrix having 3 rows and 131,584columns, as shown in FIG. 21 . We then multiple the inverse of the bandseparation matrix M⁻¹ with the combined matrix to get the resultingcomponent separated matrix of the same size, also with 3 rows and131,584 columns. The component separated matrix corresponds to the leftside of the equation (31). We then reshape the three rows of theresulting matrix to obtain the non-redundant left halves of the matricescorresponding to three frequency components {tilde over (S)}(k) {tildeover (H)}(k); {tilde over (S)}(k−p_(θ)) {tilde over (H)}(k); {tilde over(S)}(k+p_(θ)) {tilde over (H)}(k). The three non-redundant matrices areillustrated in FIG. 22 (and FIG. 18 ) as 1831, 1833, and 1835. In ourcase, the separated components are in the form of non-redundant lefthalf matrices.

Now referring to FIG. 18 , after process step 3-b, the full matrix forthe so-called DC frequency component can be represented as a squarematrix that combines the non-redundant half matrix 1831 (left half) andthe redundant half matrix 1832 (right half), or it can be a rectangularmatrix. The DC frequency matrix does not undergo the same shifting asthe high frequency matrices, so the DC halves can be assembled earlieror later without computational penalty. For the two high frequencycomponents, the symmetries are differently handled, to reduce thecomputation load.

Following process step 3-b, the full matrix of high frequency componentsis organized to combine parts from two bands, such as the non-redundanthalf matrix (1) 1843 from the first high frequency image and theredundant half matrix (2′) 1844 from the second high frequency image.Equivalently, the non-redundant half matrix (2) 1845 from the secondhigh frequency component and the redundant half matrix (1′) 1846 fromthe first high frequency component can be combined.

While three full matrices are illustrated in FIG. 18 for DC and highfrequency components, operations in steps 3b, 3c and 3d can be processedexploiting symmetries, processing the bands of frequency components byusing half of the data.

A similar process can be applied to five images with 1D structuredillumination with phase steps along an angle, separated into frequencycomponents, or a larger number of images along an angle. A functionother than sine or cosine varying illumination can be used. Thetechnology disclosed also can be applied to sets of images created with2D structured illumination.

Step 3-c (Apply Modulation Transfer Function):

Two alternatives for application of the modulation transfer function(MTF) and/or the optical transfer function (OTF) are described: atwo-step application of an independent MTF, followed by Wiener filteringto reduce noise, and a one-step application of an MTF built into aWiener filter. Both of these alternatives can take advantage of thedisclosed non-redundant calculations technology.

In this two-step implementation, we multiply the (MTF), which is theamplitude or absolute value of the optical transfer function (OTF), witheach of the three components {tilde over (S)}(k) {tilde over (H)}(k);{tilde over (S)}(k−p_(θ)) {tilde over (H)}(k); {tilde over (S)}(k+p_(θ)){tilde over (H)}(k) obtained after step 3-b. Note that steps 3-b and 3-care merged shown in FIG. 18 , as application of the MTF does not impactsymmetries in the matrices.

The modulation transfer function of a lens is a measurement of itsability to transfer contrast at a particular resolution from the objectto the image. In other words, MTF is a way to incorporate resolution andcontrast into a single specification. The resolution is an imagingsystem's ability to distinguish object detail. It can be expressed interms of line-pairs per millimeter (1 p/mm) where a line pair is asequence of one black and one white line. This measure of line-pair permillimeter is also known as frequency. The inverse of the frequencyyields the spacing in millimeters between the two resolved lines. Bartargets with a series of equally spaces, alternating white and blackbars (also known as 1951 USAF target or a Ronchi ruling) are often usedfor testing system performance. As line spacing decreases (i.e.,frequency increases on the target, it becomes increasingly difficult forthe lens to efficiently transfer this decrease in contrast; as a result,the MTF decreases.

For further details of modulation transfer function, the reader isreferred to an article titled, “Introduction to Modulation TransferFunction” available athttps://www.edmundoptics.com/resources/application-notes/optics/introduction-to-modulation-transfer-function/.FIG. 5 in the above referenced article shows that as the spatialfrequency of the lines increases, the contrast of the image decreases.For the image to appear defined, black must be truly black and whitetruly white, with a minimal amount of grayscale between. FIG. 6 of thisarticle plots the MTF of an aberration-free image with a rectangularlens. The MTF decreases as the spatial resolution increases. It isimportant to note that actual systems are not completelyaberration-free.

This step two-step application of the MTF and Wiener filtering can bereplaced with the single step approach, which is presented at step 15 ofSIM-RA Algorithm 1 of Lal et al. 2015. Again, step 3-c is the first partof an algorithm performs the Wiener filtering in two steps.Alternatively, this step can be performed after step 3-d without anyimpact on the quality of the SIM reconstructed image.

This two-step approach (i.e., applying MTF separately from the Wienerfiltering) has been recommended in the SIM image reconstructionliterature. For example, M.G.L. Gustafsson has recommended this two-stepapproach in his paper titled, “Surpassing the lateral resolution limitby factor of two using structured illumination microscopy,” published inJournal of Microscopy, Vol. 198, Pt. 2, May 2000, pp-82-87. For SIMimage reconstruction, this paper suggests multiplying each of the threefrequency components by OTF of the microscope. Following this, thecomponents are added where they overlap. Finally, the sum of thecomponents is divided by the sum of the squares of the OTF and a smallconstant. The paper further states that this process is equivalent toone-step Wiener filtering.

Wiener filtering of three components in equations 32, 33 and 34 as shownbelow (taken from Lal et al. 2015):

S ˜ u ( k ) = [ H ∼ * ( k ) ❘ "\[LeftBracketingBar]" H ∼ ( k ) ❘"\[RightBracketingBar]" 2 + Ψ o , θ 2 ❘ "\[LeftBracketingBar]" k ❘"\[RightBracketingBar]" - 2 ⁢ α ] ⁢ S ˜ ( k ) ⁢ H ∼ ( k ) ( 32 ) S ˜ u (k - p θ ) = 1 m [ H ∼ * ( k ) ❘ "\[LeftBracketingBar]" H ∼ ( k ) ❘"\[RightBracketingBar]" 2 + Ψ p , θ m 2 2 ❘ "\[LeftBracketingBar]" k - pθ ❘ "\[RightBracketingBar]" - 2 ⁢ α ] ⁢ S ˜ ( k - p θ ) ⁢ H ∼ ( k ) ( 33 )S ˜ u ( k + p θ ) = 1 m [ H ∼ * ( k ) ❘ "\[LeftBracketingBar]" H ∼ ( k )❘ "\[RightBracketingBar]" 2 + Ψ q , θ m 2 2 ❘ "\[LeftBracketingBar]" k +p θ ❘ "\[RightBracketingBar]" - 2 ⁢ α ] ⁢ S ˜ ( k + p θ ) ⁢ H ∼ ( k ) ( 34)

The three frequency components {tilde over (S)}u(k) {tilde over (H)}(k);{tilde over (S)}u(k−p_(θ)) {tilde over (H)}(k); {tilde over(S)}u(k+p_(θ)) {tilde over (H)}(k) obtained on the left side ofequations 32, 33, and 34 are referred to by Lal et al. 2015 as so-calledungraded estimates of the noisy estimates {tilde over (S)}(k) {tildeover (H)}(k); {tilde over (S)}(k−p_(θ)) {tilde over (H)}(k); {tilde over(S)}(k+p_(θ)) {tilde over (H)}(k) by applying Wiener filtering. The termungraded appears to be borrowed from ungraded meshes of finite elementanalysis. SIM-RA Algorithm 1 of Lal et al. 2015 operates with theassumption that raw SIM images are corrupted by white noise. Theexpression in square bracket in equations 32, 33, and 34 is referred toas Wiener filter and m is the modulation factor.

The noisy estimates {tilde over (S)}(k) {tilde over (H)}(k); {tilde over(S)}(k−p_(θ)) {tilde over (H)}(k); {tilde over (S)}(k+p_(θ)) {tilde over(H)}(k) are referred to as degraded images by Gonzales and Woods intheir book “Digital Image Processing” in section 5.7 on page 351.Gonzales and Woods refer to OTF {tilde over (H)}(k) as a degradationfunction. In an ideal system with no noise, a simple “inverse filter”can be applied to retrieve the undegraded image. However, in most realsystems noise is present in the acquired images. Therefore, the inversefilter cannot be used to recover undegraded components {tilde over(S)}(k), {tilde over (S)}(k−p_(θ)), and {tilde over (S)}(k+p_(θ)). AWiener filter recovers a graded image such that the mean squared errorbetween the degraded image and the graded image is minimized. Therefore,Wiener filter is also referred to as “minimum mean square error” filter.Further details of the Wiener filter are presented in section 5.8 onpage 352 of Gonzales and Woods, “Digital Image Processing”.

The Wiener filtering or the two-step method of Gustafsson enables us torecover a close approximation of undegraded components which arereferred to as ungraded estimates on the left sides of equations 32, 33,and 34 of Lal et al. 2015 as shown above.

Another important observation from equations 32, 33, and 34 of Lal etal. 2015 is that the numerators of the equations 32, 33, and 34 areconjugates of the OTF {tilde over (H)}(k). If point spread function(PSF) H(r) of the optical system is real and symmetrical then it impliesthat OTF {tilde over (H)}(k) is real. For real values, the OTF is equalto its conjugate, i.e., {tilde over (H)}(k)={tilde over (H)}*(k).Further if the OTF is positive, the MTF (the absolute value of OTF) isequal to the OTF, i.e., |H(k)|={tilde over (H)}(k). From our previousobservation, this further implies that |{tilde over (H)}(k)|={tilde over(H)}(k). This observation shows that MTF can be used as numerator inequation 32, 33, 34 (when OTF is real and positive) which means thisprocess step 3-c is equivalent to multiplying the numerator of equations32, 33, and 34 with the three degraded components {tilde over (S)}(k){tilde over (H)}(k); {tilde over (S)}(k−p_(θ)) {tilde over (H)}(k);{tilde over (S)}(k+p_(θ)) {tilde over (H)}(k) respectively in the threeequations 32, 33, and 34.

In the non-redundant SIM subtile reconstruction algorithm, we multiplythe non-redundant matrices 1831, 1833, and 1835 obtained after the bandseparation step (step 3-b). This is an element-wise scalarmultiplication and does not change the dimensions of the resultingnon-redundant matrices. After this multiplication, we get the threenon-redundant left half matrices 1831, 1833, and 1835 which are shown inFIG. 18 . After applying the OTF, the symmetries in the three fullmatrices are maintained as explained in the band separation step.

First and Middle Column Averaging

We now present a computational feature that can improve the quality ofreconstructed images using the non-redundant SIM image reconstructionalgorithm. In FIG. 23B, we illustrate the Fourier space matrices of thetwo high frequency images. In the non-redundant algorithm, we use theleft halves of the two matrices that are labeled as 1843 (left halfblock 1) and 1845 (left half block 2). These two matrices are shown inthe top row of FIG. 23B along with separated right half matrices 1846(right block 1′) and 1844 (right half block 2′). The labelling of theleft and right halves of the two matrices is consistent with FIG. 18(fourth row from the top). The non-redundant algorithm uses the lefthalves of the two high frequency matrices during image reconstruction.Due to Fourier space symmetries the right half matrices can bereconstructed using look-up of the counterpart left half matrices. Thesecond row of matrices in FIG. 23B shows stitching of left half matrixof one high frequency image to right half matrix of the second highfrequency image during image reconstruction. These stitched matrices arealso shown in 3^(rd) and 4^(th) rows of FIG. 18 .

The left half matrices of the two high frequency images in thisimplementation have two more columns than their counterpart right halfmatrices. These two columns are first and middle columns which are shownwith a hatched pattern in FIG. 23B. The first and middle columns of 1843and 1845 represent DC components that would, with perfect instrumentsand measurement, match. As the instruments used for capturing images (atdifferent phases) have imperfections and noise can also be introduced inthis process, the two high frequency matrices can have different valuesin their first and middle columns. Reconstruction can be improved byforcing the first and middle columns of 1843 and 1845 to have the samevalues.

Averaging first and middle columns of the non-redundant components ofmatrices for two high frequency components is one way to match thesecolumns in 1843 and 1845, to improve the quality of reconstructed imageby introducing symmetry in the values of these columns. FIG. 23Cillustrates averaging of the first and middle columns of left halfmatrices of the two high frequency images in Fourier space. The averagedvalues in the first and middle columns of the two matrices make theresulting matrices satisfy Fourier space symmetries. The first and lastcolumns after averaging are shown with a cross-hatch pattern in FIG.23C.

Other techniques can also be used to match values of these columns of1843 and 1845. For example, we can use values in the first and middlecolumns of either the first or the second high frequency image and usethese values in both matrices 1843 and 1845. Or a weighted combinationof the values of the first and middle columns of the two high frequencyimages can also be used. For example, combining the values by assigningtwo-thirds weight to values of one high frequency image and one-thirdweight to values of the other high frequency image. It is understoodthat different relative weights can be assigned to combine the values ofthe corresponding columns.

Step 3-d (Subpixel Shifting):

In this step, each of the high frequency components {tilde over(S)}(k−p_(θ)) {tilde over (H)}(k); {tilde over (S)}(k+p_(θ)) {tilde over(H)}(k) is shifted to overcome quantization effects of a discrete fastFourier transform. Because pixel positions in the Fourier space matrixcorrespond to discrete frequency bins, we refer this as subpixelshifting. Explaining this, Karras et. al. 2019, in their paper“Successful optimization of reconstruction parameters in structuredillumination microscopy—A practical guide”, published in OpticsCommunications, have illustrated shifting of the higher frequencycomponents into passband of the OTF when structured illumination patternis used (see FIG. 1 of Karras et. al. 2019). During subpixel shifting,the true positions of the high frequency components are centered atfrequencies p_(θ) and p_(θ) respectively in the frequency domain. Thisshifting is illustrated in equations 35 and 36 taken from Lal et. al.2015.

$\begin{matrix}{{\mathcal{F}\left\lbrack {\mathcal{F}^{- 1}\left\{ {{\overset{˜}{S}}_{u}\left( {k - p_{\theta}} \right)} \right\} \times e^{{- \overset{˙}{\iota}}2\pi{p_{\theta} \cdot r}}} \right\rbrack} = {{\overset{˜}{S}}_{s}\left( {k - p_{\theta}} \right)}} & (35)\end{matrix}$ $\begin{matrix}{{\mathcal{F}\left\lbrack {\mathcal{F}^{- 1}\left\{ {{\overset{˜}{S}}_{u}\left( {k + p_{\theta}} \right)} \right\} \times e^{{+ \overset{˙}{\iota}}2\pi{p_{\theta} \cdot r}}} \right\rbrack} = {{\overset{˜}{S}}_{s}\left( {k + p_{\theta}} \right)}} & (36)\end{matrix}$

In prior art SIM image reconstruction algorithms, such as SIM-RAAlgorithm 1 presented in Lal et al. 2015, two Fourier transformoperations are performed per high frequency images to shift the highfrequency content to its correct position as shown in equations 35 and36. The shift property of the Fourier Transform allows us to shift asignal. The shifting can be done by taking the information fromfrequency domain into spatial domain and then applying a multiplication(also referred to as applying a translation vector). Fourier transformis then applied to this shifted signal in spatial domain to bring itback to frequency domain. More information regarding shift property ofFourier Transforms can be found onhttp://www.thefouriertransform.com/transform/properties.php. Theshifting of the high frequency content is actually performed in the realspace by performing an inverse Fourier transform on the ungraded imagesso as not to constrain the translation vector to whole pixels in thediscrete Fourier transforms. Thus, we can achieve sub-pixel shifting inthe real space. The shifted high frequency components are thentransformed to frequency domain by applying Fourier transform.

We improve the efficiency of SIM image reconstruction and decrease therequired computational resources by using symmetries in the Fourierspace. The so-called DC frequency component does not need to be subpixelshifted as it is in a reference location, as illustrated by FIG. 18 .The high frequency component matrices are shifted relative to the DCfrequency component matrix.

In the disclosed non-redundant algorithm, we shift only one matrix ofhigh frequency components, after combining nonredundant halves of thecomplementary phase shifted matrices of high-frequency components, shownin FIG. 18 . The combined halves of high frequency components are shownas the left half 1833 and the right half 1834 or labeled 112′ matrixshown in middle column of the third row of FIG. 18 . This matrix isshifted from the Fourier domain to the spatial domain and back again,instead of two matrices in prior art SIM image reconstructionalgorithms. The shifted matrix 112′ is shown in the middle column offourth row in FIG. 18 with two arrows pointing diagonally upwardstowards top left corners. This matrix is composed of the left halfmatrix 1843 and right half matrix 1844. The second shifted highfrequency matrix which is composed of left half matrix 1845 and 1846 orlabeled as 2|1′ is obtained using Fourier space symmetries andconjugation of the 1|2′ matrix. We shift it the same amount as shiftingin 1|2′ but in opposite direction which is indicated by two arrowspointing diagonally towards bottom right corner.

The processes steps 3a to 3d are repeated in a loop across illuminationpeak angles. In this example, we are using two angles or orientations,so we perform the above steps for the second set of three imagesacquired with a second orientation of the structured illuminationpattern.

Processing Step 4: Reconstruct Sum and Build Wiener Filter

The Wiener filter can be built once and then applied repeatedly tofilter images even if the parameters change. The supplementary materialfor Karras et. al. 2019 paper, titled, “Successful optimization ofreconstruction parameters in structured illumination microscopy—Apractical guide,” published in Optics Communications, illustrates tuningof the Wiener filter parameter (w), which is the constant part of thedenominator in equations 32, 33, and 34. Figure Si of this supplementarymaterial for Karras et. al. 2019 illustrates impacts of different valuesof the Wiener filter parameter (w) on retrieved image spectra. A lowvalue of the Wiener filter parameter can result in noise in theretrieved spectra. Too high a value can result in loss of image data.The parameter value for a Wiener filter can be empirically chosen, asdiscussed in Lal et al. 2015 and Karras et. al. 2019 supplementarymaterials. Other type of Fourier space low pass filters canalternatively, be used to reduce noise. For example, Gaussian low passfilter, Butterworth low pass filter or Hamming low pass filter. Detailsof these filters are available onhttp://fourier.eng.hmc.edu/e101/lectures/Fourier_Analysis/node10.html,

Only parts of the matrices that include high-frequency componentsoverlap with the so-called DC frequency matrix. The expanded matrix inwhich coefficients from the multiple Fourier domain frequency matricesare combined is wider and taller from the individual matrices beforecombination by an upsample factor, which reflects the image resolutionenhancement. The upsample factor can be between 1.0 to 2.0 of the widthand height of the individual matrices. When the upsample factor is 1.0,there is no image resolution enhancement. There are no high frequencycomponents and images taken at all phases along an orientation overlapwith each other. When the upsample factor is 2.0, the shifted matricesof high frequency components are separated from each other such thattheir edges (or borders) touch at the center of the unshifted matrix.This means that the width and height of the high-resolutionreconstructed image is increased two times that of individual imagematrices. This combined matrix is illustrated as a large matrix 1850 inFIG. 18 . The matrix 1850 is illustrated twice as wider and taller thanthe individual matrices labeled 112′ and 211′ to show that thereconstructed image is enhanced. When the upsample factor for imageresolution enhancement is greater than 1.0 and less than 2.0, theshifted matrices 112′ and 211′ are positioned closer and overlap eachother at the center of the large matrix 1850.

In FIG. 18 , we illustrate constructing matrix 1850 from matrices forthree images at the first angle. The non-redundant half for the DCcomponent for first angle is shown as 1841 in FIG. 18 . We then placethe subpixel shifted coefficients for one high frequency image on thetop left part of the matrix 1850 and for the other high frequency imagein the bottom right part. Redundant conjugate components needed tocomplete 1850 can be looked up in the complementary non-redundantcomponents, keeping track of matrix positions of complementarycomponents (coefficients), and applying a changed sign to the anyimaginary part of copied coefficients.

Adding a second, orthogonal angle and three more images (which are notshown) would fill out the square. We place the full matrix for one highfrequency component for the second angle at the bottom left part of thematrix 1850 and for the second high frequency component at the top rightpart.

Combining nine images is illustrated in Lal et al. 2015. Coefficients atoverlapping matrix positions are combined in matrix 1850. This is thesix-image variation, for two angles instead of three.

Visualization of combining matrices is clearest when the coefficientsare depicted as center shifted, with the mid-value of the frequencyrange in the center of the matrix, but calculations may actually beperformed using corner shifted matrices, with the lowest frequencycomponent in a top left or bottom right corner, for instance. Thereconstruction of enhanced resolution image matrix 1850 is illustratedin FIG. 18 by placing low frequency components at the center and highfrequency components away from the center. This arrangement of frequencycontent is referred to as center shifted spectrum. Application of FastFourier Transform (FFT) to image data in spatial domain results incorner shifted spectrum in which amplitudes of low frequency componentsend up at the corners of the two-dimensional spectrum in Fourier space(or frequency domain). High frequency components are positioned closerto the center of the two-dimensional spectrum. For ease ofunderstanding, it is useful to visualize the frequency content infrequency domain as center shifted spectrum in which low frequencycomponents are positioned close to the center and high frequencycomponents are positioned away from the center. Symmetries of quadrantsin corner shifted spectrum allow swapping of the quadrants diagonally tomove the low frequency components closer to the center. Therefore,corner shifted spectrum can be converted to center shifted spectrum in astraightforward manner and vice versa. The conjugated symmetries andredundant regions hold in both corner shifted and center shiftedspectrum. The center shifted spectrum in FIG. 18 is presented for easeof understanding of the reader.

A Wiener filter can be built for the combined image using the one step(Lal et al. 2015) or the two step (Gustafsson 2000) techniques presentedabove for the band separated images in Step 3-c. For example, in oneimplementation, the Wiener filter can be built using an MTF (modulationtransfer function) which is the amplitude or absolute value of the OTF(optical transfer function). In this implementation, we perform thereconstruction process to computationally simulate the overlappingfrequency information in the Wiener filter. We apply the Wiener filterweights for band separated images to the OTF of the combined image tobuild the Wiener filter for the combined image. The Wiener filter isapplied to the combined image in the next step.

Processing Step 5: Apply Wiener Filter and Apodization Filters

In this step, we apply the Wiener filter or an alternative filter builtin the previous step to the combined image in the Fourier space. In thestep 3-c, we separated Wiener filtering into two steps by applying anindependent MTF. See equations 32, 33, and 34 in the step 3-c. As atwo-step filtering process, this step involves applying remaining partsof the Wiener filter (presented in equations 32, 33, and 34) to thecombined image in the Fourier space. In an alternative embodiment, wecan apply the Wiener filter to Fourier space representations of thethree frequency components per angle before combining them to form thecombined image. When applied early, we can apply the Wiener filter toonly the non-redundant halves of the matrices for band separatedfrequency components.

The application of Wiener filter enhances the signal, but it can alsoincrease the noise in the images. An appropriately designed apodizationfilter can avoid hard edges introduced in the images introduced due tohigh frequency content. Apodization filter is a low pass filter thatremoves frequency content outside a pre-defined radius in Fourier space.Apodization filter reduces errors introduced by the Wiener filter. Otherlow pass filters such as real space Hamming low pass filter or Gaussianlow pass filter can also be applied to reduce errors introduced byWiener filter. Note that Apodization filter is optional and not used bysome SIM reconstruction algorithms such as in SIM-RA Algorithm 1 of Lalet al. 2015. The apodization filter can, alternatively, be applied tojust the non-redundant halves of the matrices for three frequencycomponents earlier in the pipeline.

Processing Step 6: Perform an Inverse FFT

In this step we apply the inverse Fourier transform to matrix 1850 forthe full image in the frequency domain. Application of an inverse fastFourier transform (IFFT) transforms the matrix components in the Fourierdomain into an image in the spatial domain (in real space). Thisresulting matrix 1860 is the high-resolution SIM reconstructed image.When subtiles are used, it is part of a larger tile.

SIM Image Reconstruction for Full FOV Image

Subtiles of high-resolution SIM images can be combined to reconstructthe high-resolution image of the full FOV. Pseudocode for oneimplementation of the technology disclosed, to reconstruct ahigh-resolution full field of view image from multiple subtile images,is presented below. The algorithm presented below is referred to as“full field of view reconstruction” algorithm (also referred to asAlgorithm 4). It extends algorithm 3 for subtile reconstruction. Ittakes as input the images acquired and the distortion coefficients forestimation parameters. Estimation of the distortion coefficients forangle and spacing parameters (input 2), and phase bias look up table(input 3) is presented above. The algorithm divides the full FOV imagesinto subtiles of equally sized regions. SIM images are reconstructed forsubtiles as presented in algorithm 3 above. Finally, the high-resolutionSIM images of subtiles are combined to reconstruct the high-resolutionSIM image of the full FOV data (algorithm step 4b).

Algorithm 4: Full Field of View Reconstruction

-   -   Input:        -   1. Collection of images, e.g. for 2 angles and 3 phases per            angle        -   2. Distortion coefficients for angle/spacing (see final            distortion calibration with quadratic fit and lookup table            algorithm)        -   3. Distortion lookup table for phase (see final distortion            calibration with quadratic fit and lookup table algorithm)        -   4. Wiener filter and apodization parameters    -   Output: Reconstructed output    -   Algorithm        -   1. Build the following images/filters (sized to match the            subregion)            -   a. OTF            -   b. Wiener            -   c. Apodization        -   2. Divide the image into equally sized subregions, e.g.            512×512            -   a. If the image is not evenly divisible by the subregion                size, at the right edge or bottom, shift the subregion                to the left (or up) such that it falls on image.            -   b. Setup book keeping for boundary subregions to ensure                only the non-overlapping part is copied out for the                right or bottom edge subregion        -   3. Estimate parameters at the center of the image using,            e.g. 512×512 window (see subtile angle and spacing mapping            algorithm and subtile phase mapping algorithm)            -   a Estimate illumination peak angle and spacing for each                angle            -   b. Estimate modulation for each angle            -   c. Estimate phase for each image            -   4. For each subregion                -   a. Reconstruct the higher resolution subregion using                    subtile reconstruction algorithm                -    i. Using grating and angle found in step 3, and the                    given distortion coefficients to estimate the local                    grating and angle                -    ii. Using the phase found in step 3 and the given                    distortion lookup table to estimate the proper phase                -    iii. Given the previously built OTF, Wiener and                    apodization for the subregion                -   b Copy output from (a) to large image                -    i. If a boundary subregion, only copy the missing                    data

In the context of previous descriptions, this pseudo code should beself-explanatory.

Potential Improvements to SIM Image Reconstruction Algorithm

We present potential improvements to the SIM image reconstructionalgorithm (SIM) disclosed in the U.S. Provisional Patent Application No.62/924,130, entitled, “SYSTEMS AND METHODS FOR STRUCTURED ILLUMINATIONMICROSCOPY” (Attorney Docket No. ILLM 1012-1); and the U.S. ProvisionalPatent Application No. 62/924,138, entitled, “INCREASED CALCULATIONEFFICIENCY FOR STRUCTURED ILLUMINATION MICROSCOPY” (Attorney Docket No.ILLM 1022-1). Both provisional applications were filed on Oct. 21, 2019.

The first potential improvement is related to phase bias look-up table.The Baseline SIM algorithm uses static phase bias look-up table. Theassumption in Baseline model is that phase bias and distortion can belearned once and stored in the look-up table. The values in the look-uptable are assumed to be stable and applicable to multiple sequencingruns. The Baseline SIM algorithm assumes that phase bias look-up table(LUT) remains stable in the spatial domain. This means that the samelook-up table can be applied across tiles of a flowcell. Our secondassumption in Baseline algorithm is that the LUT remains stable acrossmultiple sequencing runs. Experiments showed that these assumptions maynot be true for all flowcells and sequencing runs. Tiles in a flowcellcan have different tilts or other distortions. The values in look-uptable can impacted by various environmental factors that change overmultiple sequencing runs.

The “All In” SIM algorithm, therefore, includes learning the phase biaslook-up table for every sequencing run per tile, when a new flowcell isloaded into the sequencer and a sequencing run is initiated. This canincrease the processing time required in the first cycle of a sequencingrun. However, substantial improvement in error rate and percentage ofclusters that pass the filter (or % PF) metrics is observed asillustrated in the following paragraphs.

The second potential improvement is related to geometric distortion,which causes parallel lines to bow or otherwise distort. The geometricdistortion can be cause distortions in the imaging or optical path andin the flow cell. In some cases, all or some tiles in a flowcell canhave tilts and may not be flat. This can cause geometric distortion.Characterizing all tiles in a flowcell is preferred because there can bevariations in a flowcell from tile to tile. Early version of correctionsassumed that the geometric distortion can be learned once and usedacross multiple sequencing runs. Experiments showed that this assumptionmay not be true for all sequencing runs. The “All In” algorithm,therefore, includes learning the geometric distortion for every tile ina flowcell in a first cycle of a sequencing run.

The third potential improvement is related to stitching of subtileimages to form the full field of view (FOV) image. In the Baseline SIMalgorithm, when inverse Fast Fourier transform (IFFT) is applied tosubtiles in frequency domain, the edges of the subtiles are apparent inthe spatial domain. Therefore, the stitched subtiles in the full fieldof view show stitch line at the edges of subtiles. The stitch line issuecauses dark artifacts to appear along the boundary of subtiles. TheAll-In SIM algorithm solves the stitch line issue by using an overlapbetween the subtiles. For example, the subtiles can have an overlap of 5to 15 pixels. In one implementation, the subtiles have an overlap of 8pixels along the boundary. When reconstructing the full field of view(FOV) image, the “All-In” algorithm can discard the outer 8 pixels forsubtiles and use the inner region to reconstruct the full FOV image inthe spatial domain. In this implementation, the size of subtile can belarger than 512×512 pixels by adding border pixels (e.g., 8 pixels) oneach side. In another implementation, the border pixels of subtiles canbe averaged instead of discarding the border pixels.

The fourth potential improvement is related to optimization ofApodization and Weiner filters. The filter parameters depend onupsampling. The Baseline SIM algorithm uses 1.2 times upsampling whichcan shave off a partial pixel. When the subtile images are stitchedtogether in spatial domain, this can cause shifting of images andalignment issues. The shave off of a pixel is due to upsampling factor(i.e., 1.2) that produces non-integer number of pixels. The All-In SIMalgorithm uses 1.5 times upsampling to overcome the partial pixel shaveoff issue by using an upsampling factor that produces an integer numberof pixels. Thus, providing a cleaner subtile images for stitchingtogether. In other implementations, other upsampling factor values suchas 1.25 times upsampling can be used which can also produce an integernumber of pixels.

In some implementations, the technology disclosed can use dose rampingto compensate for damage to nucleotides caused by exposure cycles in thesequencing run. In dose ramping the laser power of blue and greenchannel lasers is increased.

Performance Comparison Results

We compared performance of Baseline SIM algorithm with All-In SIMalgorithm. The two algorithms are compared using the quality ofsequencing results as indicated by error rate and percentage of clusterspassing filter (% PF). The error rate metric measures the percentage ofbasecalls made that were erroneous when compared against a knownreference genome in the sequencing process. Clusters passing filter or %PF metric is an indication of signal quality from each cluster ofmolecules on the flowcell. Lower error rate and higher % PF values aredesired as they indicate good sequencing output with high qualitybasecalls. Out of more than a hundred tiles on a flow cell, we selectedresults for best tile per sequencing run to compare the algorithms.Tiles in a flowcell can have variability in terms of flatness, tilt,surface polishing and other environmental factors. The reason forselecting the best tile for comparison out of over 100 tiles is toreduce variability caused by these other external factors throughstatistical aggregation.

FIG. 25A presents error rates for the Baseline and the All-In algorithmsfor three sequencing runs, labeled as A, B and C. For each sequencingrun, the error rate for the best tile is presented for the twoalgorithms. The top bar indicates error rate for best tile in All-Inalgorithm and bottom bar indicates error rate for best tile in Baselinealgorithm for respective sequencing runs. The table at the bottom of thegraph tabulates quantitative improvements in error rate of the best tilewhen the All-In algorithm is used. For example, the best tile error ratefor sequencing run A is 1.85% when Baseline algorithm is used. The besttile error rate for the sequencing run A is 1.18% when All-In algorithmis used, indicating a higher quality sequencing process with less errorrate. Similar improvements in lowering the error rate are observed forsequencing run B and C when using the All-In algorithm.

FIG. 25B presents clusters passing filter (or % PF) results for besttile in three sequencing runs for the two algorithms. The bottom tablepresents % PF values for best tile in respective sequencing runs for twoalgorithms. The All-In algorithm results in higher % PF values for allsequencing runs. Specifically, the % PF value for best tile insequencing run A is 82.7 indicating that 82.7% of clusters on theflowcell tile are deemed good quality clusters to generate basecallinformation for. The % PF value for best tile in sequencing run is only70.1. A higher % PF value results in more clusters passing the filterthus increasing the sequencing throughput. The patterned flow cells canhave a theoretical maximum limit on the number of clusters which isequal to the number of nanowells in which clusters of molecules canform. Results in FIGS. 25A and 25B show that All-In algorithm results inlower error rates and higher throughputs (due to higher % PF values) ascompared to the Baseline algorithm.

FIGS. 26A, 26B and 26C presents best tile error rate for cycles insequencing runs A, B, and C, respectively. Separate time series of errorrates in sequencing runs are presented for Baseline and All-Inalgorithms. The results in FIGS. 26A to 26C illustrate that All-Inalgorithm reduces the error rates in sequencing runs. The improvement inerror rate using All-In algorithm for best tile can vary from cycle tocycle. In FIG. 26A, for initial 25 cycles, the error rates of Baselineand All-In algorithms are almost same, however as the sequencing runproceeds, the error rate for All-In algorithm reduces as compared toerror rate for Baseline Algorithm. FIG. 26B shows that the error ratefor All-In algorithm are marginally better in the initial cycles. In thelater cycles of the sequencing run, All-In algorithm performs betterthan Baseline algorithm. FIG. 26C illustrates best tile error rates forcycles in sequencing run C for the two algorithms. The performance ofAll-In algorithm is significantly better than Baseline algorithm formajority of cycles. The graphs also illustrate particular cycles forwhich All-In algorithm has higher error rate. These results can helptechnicians or researchers to focus on quality issues causing increasein error rate for particular cycles.

FIG. 26D presents improvements in error rate achieved by specificupdates to the All-In algorithm as compared to the Baseline Algorithm.For example, the left plot presents improvements in error rate forsequencing run A by including phase learning and stich fixing, one byone. Phase learning refers to estimation of phase bias that can capturethe relationship between phase displacement values for non-centersubtiles with respect to center subtiles. A separate phase bias lookuptable is generated and stored per tile of the flowcell. The error ratefor the Baseline algorithm for sequencing run A is 1.85 as shown infirst column of table in FIG. 24A. The error rate improvement when phaselearning is included in Baseline algorithm is 0.65 or 32% and the errorrate improvement in Baseline algorithm is 0.19 or 10% when stitch fixingis included in the Baseline model. The graph can also be interpreted asphase learning reducing error rate by 0.65 and stitch fixing reducingerror rate by 0.19, respectively in the Baseline algorithm. The residualerror rate after inclusion of two techniques is 1.18.

The graphical plot on the right in FIG. 26D presents improvements inerror rate caused by phase learning and stitch fix techniques forsequencing run B. The inclusion of phase learning in Baseline algorithmreduces the error rate by 0.227 or 33%. The inclusion of stitch fixingin Baseline algorithm reduces the error rate by 0.039 or 6%. Theresidual error rate after inclusion of both techniques is 0.414. We nowpresent a discussion of the impact of the technology disclosed when thedisclosed SIM parameter estimation and image reconstruction techniquesare applied to process images from a sequencing run.

Impact of the Technology Disclosed

Simplified examples are presented to illustrate how the technologydisclosed can reduce computation resource requirements for structuredillumination image enhancement across a flow cell over multiple cyclesand several hours. Machine capabilities vary, so round numbers have beenchosen to make the example calculations easy to follow. Keep in mindthat the technologies disclosed can be independently applied, singularlyor in combination. In some implementations, the parameters can beestimated more frequently to compensate for system instability due toenvironmental factors. For example, as described above, the “All-In” SIMalgorithm estimates phase bias lookup table in the first cycle of everysequencing run. This can increase time duration for the first cycle byup to 30 percent. However, significant performance improvement in errorrates and % PF metrics can be achieved.

Suppose a sequencing run involves 150 cycles of analysis with twoillumination frequencies at a rate of 6 minutes per cycle. The basicimage tile capture in 150 cycles takes 15 hours. Suppose that twoangles, +/−45 degrees are used for the structured illumination withthree images per angle in each cycle. The number of images per tile over150 cycles is 1800 images. Suppose there are 70 subtiles in a 7×10 grid.The processing requirement is 126,000 subtiles in 900 minutes.

In a first implementation, suppose that the system has been calibratedbefore the run and that the quadratic fits plus the lookup tablegenerated for spacing, angle and phase differences can be usedthroughout the run. In this implementation, the near-center subtilewould be subject to fringe peak estimation 1800 times. This is thenumber of times that there is a change in the flow cell position or theillumination laser source or the projected structured illuminationphase.

In this first example, the system would avoid fringe peak estimation 69out of 70 times. For 124,200 image subtiles, the other subtiles woulduse the pre-calculated relationship with the near-center subtile todetermine reconstruction parameters. The estimation of reconstructionparameters is more computationally expensive than the actualreconstruction. So computational requirements are reduced by more thanhalf. Further reduction can be achieved using the outlined reliance onsymmetries.

Application of symmetries allows conjugate pair values to be looked up,instead of calculated by FFT and other methods. Special care needs to betaken with subtile sections that have reflected symmetries, instead ofthe 180-degree rotation and sign change operation symmetry thatdominates. With an even number of rows and columns in corner shiftedFourier transform space, the top row and the left and middle+1 columnsare handled different, due to reflected rather than rotated symmetries.See, FIG. 19 . With an odd number of rows and columns in a cornershifted Fourier transform space, the top row and the left column arehandled different, due to reflected rather than rotated symmetries. See,FIGS. 19 and 23A. Other symmetries can be applied to a center shiftedFourier transform space.

The center column of FIG. 18 illustrates how half of the calculations inthe frequency domain can be avoided at various steps, by focusing onnon-redundant data. Suppose this reduces calculations by 40 percent andimage coefficient storage by 50 percent during these steps, untilreconstruction of the whole image at 1850, 1860. The conjugate pairsneed not be included in calculations during steps 3b-3d, because valuelookups can be applied when values are need in 1850, 1860 for theconjugate pairs.

Thus, one technology disclosed can save 50 percent of requiredcomputational resources and the other can save 40 percent. Cumulatively,the computational expense of SIM reconstruction can be reduced by morethan two-thirds.

In a second implementation, suppose that the system is calibrated at thestart of a run and again at the mid-point, to update the quadratic fitsand the lookup table. Suppose that this calibration takes 8 minutes,because it involves fringe peak estimation of all 70 subtiles followedby fitting and lookup table generation. This estimation extends run timefrom 15 hours to 15 hours 16 minutes. If, instead of applying thetechnology disclosed, estimation was performed for every subtile andexposure frequency, once for every image trio, for a total of 300parameter estimations, the run time would more than triple.

In the second implementation, the impact of applying symmetries wouldnot change.

It is expected that the combined impact of the technologies disclosedwill be to allow real time processing of image tiles to be performedusing a CPU without addition of a GPU, FPGA or CGRA to the system.Alternatively, simpler computing resources can be used, even if a GPU,FPGA or CGRA implementation is chosen. This can reduce system complexityand facilitate retaining the form factor of existing machines, whileincreasing their real time processing capabilities.

Particular Implementations

Subtiles of a Full Field of View Captured by a Sensor

The subtiling technology disclosed can be described from theperspectives of calibration and/or production using a calibratedscanner.

In one calibration implementation of the technology disclosed, a methodis described for calibration or characterization of a scanner detectingflorescence of millions of samples distributed across a flow cell or,more generically, an imaging plane, in images collected over multiplecycles. Structured illumination is used to improve resolution betweenfluorescent samples positioned closer together than an Abbe diffractionlimit for optical resolution. This method of calibrating the scanner touse substantially a full field of view projected by a lens onto anoptical sensor in the scanner, includes capturing the image tiles undera structured illumination at multiple angles and phase displacements ofthe structured illumination. Variations on the method may use 2Dstructured illumination and a different phase displacement steppingpattern. The method involves calculating optical distortion across theimage tile, including measuring spacing between intensity peaks andangle of the intensity peaks in at least 9 subtiles of the image tile,including a near-center subtile, and fitting a spacing surface and anangle surface to the measured spacing and angle, respectively. Ofcourse, other configurations for dividing image tile can be used, suchas 3×3, 5×5, 5×7, 9×9, 8×11, 9×16 subtiles. Images captured by largersensors be divided into more subtiles, not limited to the subtile arrayslisted. A fitted spacing surface and fitted angle surface expresscorrections for distortions across the subtiles of the image tile.Resulting fit coefficients are saved including the fitting results thatrelate the spacing of the intensity peaks and the angle of the intensitypeaks across the at least 9 subtiles to the near center subtile.Calibrating further includes measuring phase displacements of thestructured illumination at the multiple angles and phase displacementswithin the subfields and saving a look-up table that expressesdifferences between the phase displacement in the near-center subtileand the phase displacements in other subtiles of the image tile. Thiscalibration prepares the scanner for production, for analyzing theflorescence of millions of samples distributed across the flow cell orimaging plane, in images collected over multiple cycles.

In a production implementation, the method produces enhanced images offlorescence of millions of samples distributed across a flow cell orimaging plane, from images collected over multiple cycles. This includesprocessing captured image tiles covering positions across the flow cell,the image tiles captured at multiple angles and phase displacements ofthe structured illumination, over the multiple cycles. This processingis directed to subtiles of each image tile. It includes accessing savedestimated fitting results or generating estimated fitting results thatexpress spacing and angle distortion relationships between a near-centersubtile and other subtiles. The fitting results can be coefficients of apolynomial surface fit or values in a look-up table. This method alsoincludes accessing a saved look-up table or generating a look-up tablethat expresses phase displacement differences between the near-centersubtile and the other subtiles. The fitting results and look-up tableare combined with measurements for the near-center tile of each capturedimage tile, of the spacing between intensity peaks, the angle of theintensity peaks, and the phase displacements of the structuredillumination. Reconstruction parameters are determined, on a per-subtilebasis, for each captured image tile and then applied in SIMreconstruction. This method produces enhanced resolution images for thesubtiles at the positions across the flow cell or imaging plane, overthe multiple cycles, with resolving power better than the Abbediffraction limit.

For example, for an image tile, divided into 9 subtiles, the methodincludes deriving estimated reconstruction parameters for at least 8additional subtiles of the image tile, in addition to the near-centersubtile. The estimated spacing and angle reconstruction parameters forthe additional subtiles are derived by combining the accessed orestimated parameters for the near-center subtile with saved fittingresults that relate the spacing parameters and the angle parametersacross the at least 8 additional subtiles to the near-center subtile.The estimated phase displacement reconstruction parameters for theadditional subtiles are derived by combining the accessed or estimatedparameters for the near-center subtile with a saved look-up table forthe phase displacement measurement across the at least 8 additionalsubtiles, relative to the near center subtile. As described above, otherconfigurations for dividing the image tile can be used without impactingthe parameter estimation process described above.

The enhanced resolution images can be used to sequence the samples ofover multiple cycles or, otherwise, to study florescence over an imageplane captured by dozens, hundreds or even thousands of image tiles.

In a combined calibration and production implementation, the actions ofdescribed above are combined, so the scanner is calibrated and then usedin production. The technology disclosed includes a method implementationthat improves performance of a scanner detecting florescence of millionsof samples distributed across a flow cell, in images collected overmultiple cycles. Structured illumination is used to improve resolutionbetween fluorescent samples positioned closer together than an Abbediffraction limit for optical resolution. Applying this technology,calibrating the scanner using substantially a full field of viewprojected by a lens onto an optical sensor in the scanner, includescapturing the image tiles under a structured illumination at multipleangles and phase displacements of the structured illumination. Itinvolves calculating optical distortion across the image tile, includingmeasuring spacing between intensity peaks and angle of the intensitypeaks in at least 9 subtiles of the image tile, including a near-centersubtile, and fitting a spacing surface and an angle surface to themeasured spacing and angle, respectively. Of course, image tile can bedivided into more subtiles as described above. A fitted spacing surfaceand angle surface express corrections for distortions across thesubtiles of the image tile. Resulting coefficients of the fit are saved.Calibrating further includes measuring phase displacements of thestructured illumination at the multiple angles and phase displacementswithin the subfields and saving a look-up table that expressesdifferences between the phase displacement in the near-center subtileand the phase displacements in other subtiles of the image tile.

The method progresses with processing captured image tiles coveringpositions across the flow cell, captured the image tiles at multipleangles and phase displacements of the structured illumination, over themultiple cycles. Doing this includes combining the saved fitting resultsand the saved look-up table with measurements for each captured imagetile, in the near-center tile, of the spacing between intensity peaks,the angle of the intensity peaks, and the phase displacements of thestructured illumination, to determine reconstruction parameters for eachcaptured image tile, on a per-subtile basis. It further includesproducing enhanced resolution images for the subtiles at the positionsacross the flow cell, over the multiple cycles, with resolving powerbetter than the Abbe diffraction limit and using the enhanced resolutionimages to sequence the samples of over multiple cycles.

Each of the implementations of the combined calibration and productionimplementation presented above has separate utility. For instance, afirst entity could deliver and calibrate the scanner for use by a secondentity. The first entity could use a different calibration approach todetermine and express spacing and angle distortion relationships andphase displacement differences between the near-center subtile and othersubtiles, and the production technology described could still be used.The calibration technology described could be used, then the secondentity could use the calibrated scanner in a different manner. Thus, thetechnology disclosed includes the calibration and/or productionimplementations.

This method and other implementations of the technology disclosed caneach optionally include one or more of the following features and/orfeatures described in connection with additional methods disclosed. Inthe interest of conciseness, the combinations of features disclosed inthis application are not individually enumerated and are not repeatedwith each base set of features. The reader will understand how featuresidentified in this section can readily be combined with sets of basefeatures identified as implementations.

When the imaging plane is a flow cell, the samples can be distributed inmillions of nanowells across the flow cell. At least some adjoiningpairs of nanowells can be positioned closer together than an Abbediffraction limit for optical resolution. Structure illumination allowssuch close positioning. Alternatively, the samples can be randomlydistributed across the flow cell.

The subtiles each include at least 512 by 512 pixels of the opticalsensor. Larger or smaller numbers can be used, including 256, 400, 1024,2048 and 4096 or in a range of 256 to 4096 pixels. Performance can beimproved when the subtile dimension is a power of 2, practical rangesthat we have found to work well are 512 and 1024 pixels.

The subtiles can overlap by at least 2 pixels of the optical sensor.Larger or smaller numbers can be used. For example, for a 512-pixel widewindow, up to a 256-pixel overlap could be used, and for 1024 pixelswide, up to a 512 overlap could be used.

Results of fitting the spacing surface and the angle surface can bestored as coefficients of a quadratic, cubic or higher order surface, orin a look-up table calculated from polynomial fits of the spacing andangle surfaces.

In some implementations, re-measuring the spacing between intensitypeaks and angle of the intensity peaks in the subtiles, refitting thespacing surface and the angle surface, and saving the re-fitting resultsfor use in processing subsequently captured image tiles can be performedone or more times during a sequencing run. This can be done, forinstance, at an intermediate cycle such as the 50^(th) cycle or lateramong at least 100 cycles. In some implementations, re-measuring can beperformed when computing resources are available such as at the end of asequencing run or during the time between two reads when chemistry stepis being performed for the next read.

Nanowells can be arranged in a regular repeating pattern. For arectangular pattern, two structured illumination angles can be used,substantially along two diagonals connecting opposing corners of arectangle in the pattern, so that intensity peaks of the structuredillumination are oriented substantial normal to the two diagonals. For arepeating hexagonal pattern of nanowells, with three diagonalsconnecting opposing corners of hexagons in the pattern, three structuredillumination angles can be used with intensity peaks that are orientedsubstantial normal to the three diagonals. Alternatively, samples can berandomly distributed over an imaging plane without nanowells. Or, thesamples could be regularly arranged over the imaging plane by some meansother than nanowells.

The method can further include aggregating the enhanced resolutionimages for subtiles into an enhanced resolution image for a tile andusing the enhanced resolution image tile for further analysis. Thefurther analysis can include sequencing the samples, one position percycle.

As an improvement on fitting the surfaces, the method can furtherinclude determining a cropping margin and applying the cropping marginto remove from calculations pixels around edges of the sensor fromconsideration, before fitting of the spacing surface and the anglesurface to the measured spacing and angle. The margin can be uniformaround edges of the sensor or it can vary by edge, such as wider at theends of a rectangular sensor than along the long edge.

The method can further include measuring spacing between intensity peaksand angle of intensity peaks over at least 8 by 11 subtiles of the imagetile. Other configurations for dividing the image tile into subtiles canbe used as described above.

The subtiling technology disclosed can be used to produce an enhancedresolution image from images of a target captured under structuredillumination. In one image reconstruction implementation of thetechnology disclosed, a method is described for dividing a capturedimage of the target captured by an optical sensor into 9 or moresubtiles and operating independently on each of the 9 or more subtiles.As described above, more subtiles can be used to subdivide the imagecaptured. The method includes transforming, in a respective subtile, atleast three images of the target into a Fourier domain to produce atleast three frequency domain matrices of the respective subtile. Theimages are captured by a sensor in a spatial domain while applying atleast three phase displacements of the structured illumination along oneangle. An inverse mixing matrix is calculated using estimatedreconstruction parameters. The inverse matrix is applied to thefrequency domain matrices to produce at least three phase-separatedmatrices in the Fourier domain, which are unshifted and shiftedmatrices. A subpixel shifting on at least one shifted matrix isperformed. The subpixel shifting includes transforming the shiftedmatrix from the Fourier domain into the spatial domain, applying atranslation vector to data in the spatial domain, and transforming thetranslated data from the spatial domain data. The subpixel shiftingproduces two or more realigned shifted matrices in the Fourier domain.The method includes aligning and summing overlapping values of theunshifted matrix and the realigned shifted matrices to produce anexpanded frequency coverage matrix. The expanded frequency coveragematrix is inversely transformed from the Fourier domain to produce anenhanced resolution subtile in the spatial domain. Optionally, theenhanced resolution image of the target can be produced by merging theenhanced resolution subtiles for the 9 or more subtiles.

This method and other implementations of the technology disclosed caneach optionally include one or more of the following features and/orfeatures described in connection with additional methods disclosed. Inthe interest of conciseness, the combinations of features disclosed inthis application are not individually enumerated and are not repeatedwith each base set of features. The reader will understand how featuresidentified in this section can readily be combined with sets of basefeatures identified as implementations.

The process steps of method can be applied repeatedly to produce asequence of the enhanced resolution images. The sequence of enhancedresolution images can be used to sequence samples imaged by the sensorover multiple cycles.

The subtiles each include at least 512 by 512 pixels of the opticalsensor. Larger or smaller numbers can be used, including 256, 1024, 2048and 4096 or 256-4096 pixels. Performance can be improved when thesubtile dimension is a power of 2, practical ranges that we have foundto work well are 512 and 1024 pixels.

The subtiles can overlap by at least 2 pixels of the optical sensor.Larger or smaller numbers can be used, for example for 512, a 256-pixeloverlap would be appropriate and for 1024 a 512 overlap would beappropriate.

The method can further include aggregating the enhanced resolutionimages for subtiles into an enhanced resolution aggregate image andusing the enhanced resolution aggregate to sequence the sample overmultiple cycles.

The method can be further applied to image tiles at positions across aflow cell that capture fluorescence of millions of samples distributedacross the flow cell. Such an application of the method producesenhanced images of the flow cell.

When the imaging plane is a flow cell, the samples can be distributed inmillions of nanowells across the flow cell. At least some adjoiningpairs of nanowells can be positioned closer together than an Abbediffraction limit for optical resolution. Structure illumination allowssuch close positioning. Alternatively, the samples can be randomlydistributed across the flow cell. In such an implementation, the methodfurther includes producing sequences of the enhanced images for the flowcell and using the sequences of the enhanced images to call sequences ofthe samples.

The computer implemented methods described above can be practiced in asystem that includes computer hardware. The computer implemented systemcan practice one or more of the methods described above. The computerimplemented system can incorporate any of the features of methodsdescribed immediately above or throughout this application that apply tothe method implemented by the system. In the interest of conciseness,alternative combinations of system features are not individuallyenumerated. Features applicable to systems, methods, and articles ofmanufacture are not repeated for each statutory class set of basefeatures. The reader will understand how features identified in thissection can readily be combined with base features in other statutoryclasses.

As an article of manufacture, rather than a method, a non-transitorycomputer readable medium (CRM) can be loaded with program instructionsexecutable by a processor. The program instructions when executed,implement one or more of the computer implemented methods describedabove. Alternatively, the program instructions can be loaded on anon-transitory CRM and, when combined with appropriate hardware, becomea component of one or more of the computer implemented systems thatpractice the methods disclosed.

Each of the features discussed in this particular implementation sectionfor the method implementation apply equally to CRM and systemimplementations. As indicated above, all the method features are notrepeated here, in the interest of conciseness, and should be consideredrepeated by reference.

Non-Redundant SIM Image Reconstruction Application

The technology disclosed relates to reducing computing resourcesrequired to produce an enhanced resolution image from structuredillumination of a target.

In one implementation of the technology disclosed, a method is describedfor producing an enhanced resolution image from images of a targetcaptured under structured illumination. This method applies one or moretransformations to non-redundant data and then recovers redundant datafrom the non-redundant data after the transformations.

The method includes transforming at least three images of the target,captured by a sensor in a spatial domain. This sensor captures imagesfrom at least three phase displacements of structured illumination alongone angle. Three captured images in the spatial domain are transformedinto a Fourier domain to produce at least three frequency domainmatrices. These frequency domain matrices include at least non-redundantcomponents and redundant conjugate components. The redundant conjugatecomponents are conjugates, in complementary matrix positions, of thenon-redundant components. The method further includes using estimatedreconstruction parameters to generate an inverse mixing matrix andapplying the inverse mixing matrix to at least the non-redundantcomponents. This produces at least three phase-separated matrices in theFourier domain, which are unshifted and shifted matrices. These matricesinclude non-redundant unshifted components and non-redundant shiftedspatial frequency components, derived by applying the inverse mixingmatrix to the non-redundant components of the frequency domain matrices;

The method also includes performing one or more intermediatetransformations on at least the non-redundant components of thephase-separated matrices. One intermediate operation is subpixelshifting. This includes transforming from the Fourier domain into thespatial domain at least the non-redundant shifted spatial frequencycomponents of the shifted matrices. Then applying a translation vectorto data in the spatial domain and transforming the translated data fromthe spatial domain data into one or more realigned shifted matrices backin the Fourier domain. Applying the translation vector effectivelyrealigns overlapping values in the shifted matrices in the Fourierdomain with the unshifted matrix more precisely than whole matrixpositions of discrete Fourier transforms. The redundant conjugatecomponents can be recovered, after the intermediate transformations,from the non-redundant components by copying transformed values from thenon-redundant components to the complementary matrix positions of theredundant conjugate components and changing a sign of imaginary parts ofthe copied values to produce transformed redundant conjugate components.This reduces the resources required during the intermediatetransformations. Constructing an enhanced resolution image furtherincludes aligning and summing overlapping values of the unshifted matrixand the shifted matrices to produce an expanded frequency coveragematrix, then inversely transforming the expanded frequency coveragematrix from the Fourier domain to produce an enhanced resolution imagein the spatial domain.

In some implementation, a series of enhanced resolution images areproduced by repeatedly applying the preceding actions. Optionally, forinstance in sequencing by synthesis, the technology disclosed can beapplied to using the series of enhanced resolution images, to sequencesamples imaged by the sensor of over multiple cycles.

This method and other implementations of the technology disclosed caneach optionally include one or more of the following features and/orfeatures described in connection with additional methods disclosed. Inthe interest of conciseness, the combinations of features disclosed inthis application are not individually enumerated and are not repeatedwith each base set of features. The reader will understand how featuresidentified in this section can readily be combined with sets of basefeatures identified as implementations.

Technology disclosed can be applied to components arranged in a cornershifted matrix, the complementary matrix positions of the redundantconjugate components are rotated 180 degrees from matrix positions ofthe non-redundant components.

The method can further include applying, during the intermediateoperations, of one or two-step Wiener filtering to reduce noise. Whentwo-step Wiener filtering is applied, an independent modulation transferfunction can be applied to the phase separated matrices as anintermediate transformation to compensate for optical transfer ofcontrast by an objective lens which has decreasing ability to transfercontrast with increasing spatial frequency. The noise removal componentsof the Wiener filtering can be separately applied, later. Theintermediate operations can further include applying Apodizationfiltering.

In some implementations, the phase-separated matrices in the Fourierdomain include an even number of rows and an even number of columns. Forthese even dimension matrices, column averaging can be applied as anintermediate operation to average a DC column, of the DC components,with a middle column, one column beyond the center of the evendimensioned matrix. This column averaging can precede the subpixelshifting.

In some implementations, in the phase-separated matrices in the Fourierdomain are corner shifted. Then, the DC components appear in a top row,or bottom row and a left column, or right column, of the matrices andthe redundant conjugate components appear in a block rotated 180 degreesfrom the orientation of the non-redundant components.

In some implementations, the phase-separated matrices in the Fourierdomain include an odd number of rows and an odd number of columnswithout a column between blocks of the non-redundant components and theredundant conjugate components. In odd matrices, the redundant conjugatecomponents also appear in a block rotated 180 degrees from theorientation of the non-redundant components.

In implementations wherein the target includes regularly spacednanowells arranged in a rectangular pattern, the method can be appliedto sets of at least three images from phase displacements along twoangles of the structured illumination of the target, with the two anglesare along diagonals between opposing corners of the rectangles.Alternatively, in implementations wherein the target includes regularlyspaced nanowells arranged in a hexagonal pattern, the method can beapplied to sets of at least three images from phase displacements alongthree angles of the structured illumination of the target, with thethree angles are along diagonals between opposing corners of hexagons.

The method can further include applying the Wiener filtering during theintermediate operations in two steps. When performed in two steps,includes applying an independent modulation transfer function to thephase separated matrices to compensate for optical transfer of contrast,which decreases with increasing spatial frequency, by an objective lenstransferring the target under the structured illumination to a sensor.It further includes applying noise removal components of the Wienerfiltering or another equivalent filter.

The method can further include applying an Apodization filter during theintermediate operations.

The phase-separated matrices in the Fourier domain can include an evennumber of rows and an even number of columns. For such even matrices,the method can further include applying column averaging during theintermediate operations to average a DC column, of the DC components,with a middle column, between blocks of the non-redundant components andthe redundant conjugate components. The column averaging can precede thesubpixel shifting.

When the phase-separated matrices in the Fourier domain are top leftcorner shifted, the DC components can appear in a top row and a leftcolumn of the matrices and the redundant conjugate components appear ina block rotated 180 degrees from its orientation as the non-redundantcomponents. Alternatively, the phase-separated matrices can be bottomright corner shifted.

The phase-separated matrices in the Fourier domain alternatively caninclude an odd number of rows and an odd number of columns. Such oddmatrices do not include a middle column between blocks of thenon-redundant components and the redundant conjugate components.

The method further can be applied to sets of at least three images ofthe target captured through a lens by a sensor in the spatial domain,from phase displacements along two angles of the structured illuminationof the target. When the target includes regularly spaced nanowells, thetwo angles can be oriented substantially are along diagonals betweenopposing corners of quadrilaterals among the regularly spaced nanowells.These quadrilaterals can be squares and the two angles can besubstantially orthogonal, within one to three degrees. Alternatively,the method can be applied to sets of images from phase displacementsalong three angles of the structured illumination of the target. This isuseful when the target includes spaced nanowells regularly spaced in ahexagonal pattern. With this pattern, the three angles can be alongdiagonals between opposing corners of hexagons in the pattern.

The computer implemented methods described above can be practiced in asystem that includes computer hardware. The computer implemented systemcan practice one or more of the methods described above. The computerimplemented system can incorporate any of the features of methodsdescribed immediately above or throughout this application that apply tothe method implemented by the system. In the interest of conciseness,alternative combinations of system features are not individuallyenumerated. Features applicable to systems, methods, and articles ofmanufacture are not repeated for each statutory class set of basefeatures. The reader will understand how features identified in thissection can readily be combined with base features in other statutoryclasses.

As an article of manufacture, rather than a method, a non-transitorycomputer readable medium (CRM) can be loaded with program instructionsexecutable by a processor. The program instructions when executed,implement one or more of the computer implemented methods describedabove. Alternatively, the program instructions can be loaded on anon-transitory CRM and, when combined with appropriate hardware, becomea component of one or more of the computer implemented systems thatpractice the methods disclosed.

Each of the features discussed in this particular implementation sectionfor the method implementation apply equally to CRM and systemimplementations. As indicated above, all the method features are notrepeated here, in the interest of conciseness, and should be consideredrepeated by reference.

The preceding description is presented to enable the making and use ofthe technology disclosed. Various modifications to the disclosedimplementations will be apparent, and the general principles definedherein may be applied to other implementations and applications withoutdeparting from the spirit and scope of the technology disclosed. Thus,the technology disclosed is not intended to be limited to theimplementations shown, but is to be accorded the widest scope consistentwith the principles and features disclosed herein. The scope of thetechnology disclosed is defined by the appended claims.

Computer System

FIG. 24 is a simplified block diagram of a computer system that can beused to implement the technology disclosed. Computer system typicallyincludes at least one processor that communicates with a number ofperipheral devices via bus subsystem. These peripheral devices caninclude a storage subsystem including, for example, memory devices and afile storage subsystem, user interface input devices, user interfaceoutput devices, and a network interface subsystem. The input and outputdevices allow user interaction with computer system. Network interfacesubsystem provides an interface to outside networks, including aninterface to corresponding interface devices in other computer systems.

In one implementation, a parameter estimator 2401 to estimate the threeparameters (angle, spacing and phase offset) is communicably linked tothe storage subsystem and user interface input devices.

User interface input devices can include a keyboard; pointing devicessuch as a mouse, trackball, touchpad, or graphics tablet; a scanner; atouch screen incorporated into the display; audio input devices such asvoice recognition systems and microphones; and other types of inputdevices. In general, use of the term “input device” is intended toinclude all possible types of devices and ways to input information intocomputer system.

User interface output devices can include a display subsystem, aprinter, a fax machine, or non-visual displays such as audio outputdevices. The display subsystem can include a cathode ray tube (CRT), aflat-panel device such as a liquid crystal display (LCD), a projectiondevice, or some other mechanism for creating a visible image. Thedisplay subsystem can also provide a non-visual display such as audiooutput devices. In general, use of the term “output device” is intendedto include all possible types of devices and ways to output informationfrom computer system to the user or to another machine or computersystem.

Storage subsystem stores programming and data constructs that providethe functionality of some or all of the modules and methods describedherein. These software modules are generally executed by processor aloneor in combination with other processors.

Memory used in the storage subsystem can include a number of memoriesincluding a main random access memory (RAM) for storage of instructionsand data during program execution and a read only memory (ROM) in whichfixed instructions are stored. A file storage subsystem can providepersistent storage for program and data files, and can include a harddisk drive, a floppy disk drive along with associated removable media, aCD-ROM drive, an optical drive, or removable media cartridges. Themodules implementing the functionality of certain implementations can bestored by file storage subsystem in the storage subsystem, or in othermachines accessible by the processor.

Bus subsystem provides a mechanism for letting the various componentsand subsystems of computer system communicate with each other asintended. Although bus subsystem is shown schematically as a single bus,alternative implementations of the bus subsystem can use multiplebusses.

Computer system itself can be of varying types including a personalcomputer, a portable computer, a workstation, a computer terminal, anetwork computer, a television, a mainframe, a server farm, awidely-distributed set of loosely networked computers, or any other dataprocessing system or user device. Due to the ever-changing nature ofcomputers and networks, the description of computer system depicted inFIG. 24 is intended only as a specific example for purposes ofillustrating the technology disclosed. Many other configurations ofcomputer system are possible having more or less components than thecomputer system depicted in FIG. 24 .

The deep learning processors can be GPUs or FPGAs and can be hosted bydeep learning cloud platforms such as Google Cloud Platform, Xilinx, andCirrascale. Examples of deep learning processors include Google's TensorProcessing Unit (TPU), rackmount solutions like GX4 Rackmount Series,GX8 Rackmount Series, NVIDIA DGX-1, Microsoft' Stratix V FPGA,Graphcore's Intelligent Processor Unit (IPU), Qualcomm's Zeroth platformwith Snapdragon processors, NVIDIA's Volta, NVIDIA's DRIVE PX, NVIDIA'sJETSON TX1/TX2 MODULE, Intel's Nirvana, Movidius VPU, Fujitsu DPI, ARM'sDynamicIQ, IBM TrueNorth, and others.

1. A method of generating subtile calibration parameters, used with ascanner to produce enhanced resolution images of florescence of samplespositioned closer together than an Abbe diffraction limit for opticalresolution between at least some adjoining pairs of the samples,including: calibrating the scanner using substantially a full field ofview projected by a lens onto an optical sensor in the scanner,including: capturing at least one image tile under a structuredillumination at multiple angles and phase displacements of thestructured illumination; calculating optical distortion across the imagetile, including: measuring spacing between intensity peaks and angle ofthe intensity peaks in at least 9 subfiles of the image tile, includinga near-center subtile, and fitting a spacing surface and an anglesurface to the measured spacing and angle, respectively, wherein thespacing surface and the angle surface both express corrections fordistortions across the subfiles of the image tile; and measuring phasedisplacements of the structured illumination at the multiple angles andphase displacements within the subfiles and generating a look-up tablethat expresses differences between the phase displacement in thenear-center subtile and the phase displacements in other subfiles of theimage tile; and saving the subtile calibration parameters, including thefitting results that relate the spacing of the intensity peaks and theangle of the intensity peaks across the at least 9 subfiles to thenear-center subtile, and the look-up table of the differences in thephase displacement across the at least 9 subfiles, relative to thenear-center subtile, for use by the scanner in production.
 2. The methodof claim 1, wherein the samples include millions of samples distributedacross a flow cell, in images collected over multiple cycles.
 3. Themethod of claim 1, wherein the subfiles each include at least 512 by 512pixels of the optical sensor.
 4. The method of claim 1, wherein thesubfiles overlap by at least 2 pixels of the optical sensor.
 5. Themethod of claim 1, further including saving results of the fitting ofthe spacing surface and the angle surface as coefficients of a quadraticsurface.
 6. The method of claim 1, further including saving results ofthe fitting of the spacing surface and the angle surface as coefficientsof a cubic surface.
 7. The method of claim 1, further including savingresults of the fitting in a look-up table calculated from quadraticfitting of the spacing surface and the angle surface.
 8. The method ofclaim 1, further including saving results of the fitting in a look-uptable calculated from cubic fitting of the spacing surface and the anglesurface.
 9. The method of claim 1, further including applying a croppingmargin to eliminate pixels around edges of the sensor from use infitting the spacing surface and the angle surface.
 10. The method ofclaim 1, further including measuring spacing between intensity peaks andangle of the intensity peaks over at least 8 by 11 subtiles of the imagetile.
 11. A method of using a calibrated scanner to produce enhancedresolution images of samples positioned closer together than an Abbediffraction limit for optical resolution between at least some adjoiningpairs of the samples, including: for a near-center subtile of an imagetile captured by an optical sensor, accessing or generating estimatedreconstruction parameters, including: spacing parameters, for thespacing between intensity peaks, and pattern angle parameters, for theangle of the intensity peaks, the spacing parameters generated frommeasurements of imaging a regularly spaced and angled structuredillumination pattern projected onto the near-center subtile, and phasedisplacement parameters for relative phase relationship among thesubtiles, at a subtile reference point, generated from furthermeasurements of imaging multiple phase displacements along one angle ofthe structured illumination pattern projected onto the near-centersubtile; deriving estimated reconstruction parameters for at least 8additional subtiles of the image tile, in addition to the near-centersubtile, by combining the accessed or estimated reconstructionparameters for the near-center subtile with: saved fitting results thatrelate the spacing parameters and the angle parameters across the atleast 8 additional subtiles to the near-center subtile, and a savedlook-up table for the with phase displacement measurements across the atleast 8 additional subtiles, relative to the near-center subtile; andapplying the estimated reconstruction parameters for the near-centersubtile and for the at least 8 additional subtiles to produce enhancedresolution images for the subtiles with resolving power better than theAbbe diffraction limit.
 12. The method of claim 11, further includingrepeatedly applying actions of claim 41 to produce a sequence of theenhanced resolution images for the subtiles and using the sequence ofthe enhanced resolution images to sequence samples imaged by the sensorover multiple cycles.
 13. The method of claim 11, wherein the subtileseach include at least 512 by 512 pixels of the optical sensor.
 14. Themethod of claim 11, wherein the subtiles overlap by at least 2 pixels ofthe optical sensor.
 15. The method of claim 11, further includingaggregating the enhanced resolution images for the subtiles into anenhanced resolution image for the tile and using the enhanced resolutionimage for the tile to sequence the samples over multiple cycles.
 16. Themethod of claim 11, further including, at an intermediate cycle amongthe multiple cycles, re-measuring the spacing between intensity peaksand angle of the intensity peaks in the subtiles, refitting the spacingsurface and the angle surface, and saving results of the refitting foruse in processing subsequently captured image tiles.
 17. The method ofclaim 16, wherein the intermediate cycle is a 50th cycle or later amongat least 100 cycles.
 18. The method of claim 11, further applied toimage tiles at positions across a flow cell that capture florescence ofmillions of samples distributed across the flow cell, includingproducing the enhanced images for the flow cell.
 19. The method of claim18, wherein the samples are distributed in millions of nanowells acrossthe flow cell and at least some adjoining pairs of nanowells arepositioned closer together than an Abbe diffraction limit for opticalresolution.
 20. The method of claim 11, further including producingsequences of the enhanced images for the flow cell, and using thesequences of the enhanced images to call sequences of the samples. 21.The method of claim 20, wherein the nanowells are arranged in arepeating rectangular pattern with two diagonals connecting opposingcomers of a rectangle in the pattern, further including using twostructured illumination angles at which the intensity peaks are orientedsubstantial normal to the two diagonals.
 22. The method of claim 20,wherein the nanowells are arranged in a repeating hexagonal pattern withthree diagonals connecting opposing comers of a hexagon in the pattern,further including using three structured illumination angles at whichthe intensity peaks are oriented substantial normal to the threediagonals.
 23. The method of claim 11, wherein the applying theestimated reconstruction parameters comprises: applying an inversemixing matrix to one or more frequency domain matrices to produce atleast three phase-separated matrices in the Fourier domain using theestimated reconstruction parameters, the at least three phase-separatedmatrices comprising unshifted and shifted matrices; performing subpixelshifting on at least one shifted matrix, including: transforming theshifted matrix from the Fourier domain into the spatial domain, applyinga translation vector to data in the spatial domain, transforming thetranslated spatial domain data from the spatial domain data to producetwo or more realigned shifted matrices in the Fourier domain; aligningand summing overlapping values of the unshifted matrix and the realignedshifted matrices to produce an expanded frequency coverage matrix; andinversely transforming the expanded frequency coverage matrix from theFourier domain to produce the enhanced resolution images for thesubtiles.